Data processing systems for validating authorization for personal data collection, storage, and processing

ABSTRACT

In particular embodiments, a data processing consent management system may be configured to utilize one or more age verification techniques to at least partially authenticate the data subject&#39;s ability to provide valid consent (e.g., under one or more prevailing legal requirements) in order to collect, store, and or process the subject&#39;s personal data. For example, according to one or more particular legal or industry requirements, an individual (e.g., data subject) may need to be at least a particular age (e.g., an age of majority, an adult, over 18, over 21, over 13, or any other suitable age) in order to provide valid consent. Data processing systems may generate and store one or more consent records memorializing valid consent for data processing from data subjects in response to confirming that the data subject is old enough to provide such consent.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 17/196,570, filed Mar. 9, 2021, which claims priority from U.S. Provisional Patent Application Ser. No. 62/987,136, filed Mar. 9, 2020, and is also a continuation-in-part of U.S. patent application Ser. No. 17/101,915, filed Nov. 23, 2020, now U.S. Pat. No. 11,126,748, issued Sep. 21, 2021, which is a continuation of U.S. patent application Ser. No. 16/778,709, filed Jan. 31, 2020, now U.S. Pat. No. 10,846,433, issued Nov. 24, 2020, which is a continuation-in-part of U.S. patent application Ser. No. 16/560,963, filed Sep. 4, 2019, now U.S. Pat. No. 10,726,158, issued Jul. 28, 2020, which claims priority to U.S. Provisional Patent Application Ser. No. 62/728,432, filed Sep. 7, 2018, and is also a continuation-in-part of U.S. patent application Ser. No. 16/277,568, filed Feb. 15, 2019, now U.S. Pat. No. 10,440,062, issued Oct. 8, 2019, which claims priority from U.S. Provisional Patent Application Ser. No. 62/631,684, filed Feb. 17, 2018 and U.S. Provisional Patent Application Ser. No. 62/631,703, filed Feb. 17, 2018, and is also a continuation-in-part of U.S. patent application Ser. No. 16/159,634, filed Oct. 13, 2018, now U.S. Pat. No. 10,282,692, issued May 7, 2019, which claims priority from U.S. Provisional Patent Application Ser. No. 62/572,096, filed Oct. 13, 2017 and U.S. Provisional Patent Application Ser. No. 62/728,435, filed Sep. 7, 2018, and is also a continuation-in-part of U.S. patent application Ser. No. 16/055,083, filed Aug. 4, 2018, now U.S. Pat. No. 10,289,870, issued May 14, 2019, which claims priority from U.S. Provisional Patent Application Ser. No. 62/547,530, filed Aug. 18, 2017, and is also a continuation-in-part of U.S. patent application Ser. No. 15/996,208, filed Jun. 1, 2018, now U.S. Pat. No. 10,181,051, issued Jan. 15, 2019, which claims priority from U.S. Provisional Patent Application Ser. No. 62/537,839, filed Jul. 27, 2017, and is also a continuation-in-part of U.S. patent application Ser. No. 15/853,674, filed Dec. 22, 2017, now U.S. Pat. No. 10,019,597, issued Jul. 10, 2018, which claims priority from U.S. Provisional Patent Application Ser. No. 62/541,613, filed Aug. 4, 2017, and is also a continuation-in-part of U.S. patent application Ser. No. 15/619,455, filed Jun. 10, 2017, now U.S. Pat. No. 9,851,966, issued Dec. 26, 2017, which is a continuation-in-part of U.S. patent application Ser. No. 15/254,901, filed Sep. 1, 2016, now U.S. Pat. No. 9,729,583, issued Aug. 8, 2017, which claims priority from: (1) U.S. Provisional Patent Application Ser. No. 62/360,123, filed Jul. 8, 2016; (2) U.S. Provisional Patent Application Ser. No. 62/353,802, filed Jun. 23, 2016; (3) U.S. Provisional Patent Application Ser. No. 62/348,695, filed Jun. 10, 2016. The disclosures of all of the above patent applications are hereby incorporated herein by reference in their entirety.

BACKGROUND

Over the past years, privacy and security policies, and related operations have become increasingly important. Breaches in security, leading to the unauthorized access of personal data (which may include sensitive personal data) have become more frequent among companies and other organizations of all sizes. Such personal data may include, but is not limited to, personally identifiable information (PII), which may be information that directly (or indirectly) identifies an individual or entity. Examples of PII include names, addresses, dates of birth, social security numbers, and biometric identifiers such as a person's fingerprints or picture. Other personal data may include, for example, customers' Internet browsing habits, purchase history, or even their preferences (e.g., likes and dislikes, as provided or obtained through social media).

Many organizations that obtain, use, and transfer personal data, including sensitive personal data, have begun to address these privacy and security issues. To manage personal data, many companies have attempted to implement operational policies and processes that comply with legal and industry requirements. However, there is an increasing need for improved systems and methods to manage personal data in a manner that complies with such policies.

SUMMARY

A method, according to various aspects, comprises: (1) responsive to a request to initiate a transaction between an entity and a data subject, generating a consent receipt set comprising a consent receipt identifier, a transaction identifier based on the transaction, and a subject identifier based on the data subject; (2) requesting, by the computing hardware, at least one piece of identifying information regarding the data subject; (3) receiving, by the computing hardware, the at least one piece of identifying information comprising an image of the data subject; (4) determining, by the computing hardware, based at least in part on the at least one piece of identifying information, an age of the data subject by: (A) causing an artificial intelligence image system to generate a prediction usable for determining the age of the data subject by providing the image of the data subject to the artificial intelligence image system for analysis; and (B) receiving, from the artificial intelligence image system, the prediction; (5) determining, by the computing hardware, based on the age of the data subject and the transaction, whether the data subject meets one or more age criteria for processing personal data under the transaction; and (6) in response to determining the data subject does not meet the one or more age criteria for the processing of personal data under the transaction, modifying, by the computing hardware, the consent receipt set to prevent a computing system from providing the data subject with access to functionality requiring valid consent.

In particular aspects, the consent receipt set comprises a consent status and generating the consent receipt set comprises initially setting, by the computing hardware, the consent status to valid in response to the request to initiate the transaction. In still other aspects, modifying the consent receipt set to prevent the computing system from providing the data subject with access to the functionality requiring valid consent comprises modifying, by the computing hardware, the consent status to invalid in response to determining the data subject does not meet the one or more age criteria. In some aspects, the method further comprises: (1) determining, by the computing hardware, image capture data associated with the image of the data subject, the image capture data comprising at least one of a capture time of the image of the data subject or an image capture location of the image of the data subject; and (2) determining whether the data subject meets the one or more age criteria for processing the personal data under the transaction is further based on the image capture data. In various aspects, the image capture data comprises the capture time of the image of the data subject. In any aspect described herein, determining whether the data subject meets the one or more age criteria for processing the personal data under the transaction comprises determining whether the capture time of the image of the data subject is within a threshold amount of time of a time of the request to initiate the transaction.

In particular aspects, the prediction comprises a confidence level; and the method further comprises: (1) determining, by the computing hardware based on the confidence level, whether the prediction satisfies a threshold confidence level; (2) responsive to determining that the prediction does not satisfy the threshold confidence level, requesting at least one second piece of identifying information; and (3) determining, by the computing hardware, based at least in part on the at least one second piece of identifying information, the age of the data subject by: (A) causing the artificial intelligence image system to generate a second prediction usable for determining the age of the data subject by providing the second image of the data subject to the artificial intelligence image system for analysis; and (B) receiving, from the artificial intelligence image system, the second prediction. In particular aspects, the at least one second piece of identifying information comprises a second image of the data subject. In some aspects, the method further comprises: (1) causing, by the computing hardware, the artificial intelligence image system to generate a second prediction usable for determining whether the data subject is performing a particular action in the image of the data subject; and (2) determining, by the computing hardware, whether the data subject meets the one or more age criteria for processing personal data under the transaction based on the second prediction.

A system, according to various aspects, comprises: (1) a non-transitory computer-readable medium storing instructions; and (2) a processing device communicatively coupled to the non-transitory computer-readable medium. In particular aspects, the processing device is configured to execute the instructions and thereby perform operations comprising: (1) responsive to a request to initiate a transaction between an entity and a data subject, generating a consent receipt set comprising a consent receipt identifier, a transaction identifier based on the transaction, and a subject identifier based on the data subject; (2) receiving a piece of identifying information for the data subject; (3) causing a third-party artificial intelligence system to generate an output related to determining an age of the data subject by providing the piece of identifying information to the third-party artificial intelligence system for analysis; (4) receiving the output related to determining the age of the data subject from the third-party artificial intelligence system; (5) determining, based on the output, whether the data subject satisfies an age criterion for the transaction; and (6) in response to determining that the data subject does not meet the age criterion, modifying the consent receipt set to prevent a computing system from providing the data subject with access to functionality requiring valid consent.

In any aspect described herein, the output may comprise a request for additional identifying information. In particular aspects, the operations further comprise: (1) receiving the additional identifying information for the data subject; (2) causing the third-party artificial intelligence system to generate a second output related to determining the age of the data subject by providing the additional identifying information to the third-party artificial intelligence system for analysis; and (3) receiving the second output. In some aspects, determining whether the data subject satisfies the age criterion for the transaction is further based on the second output.

In a particular aspect, the output comprises at least one of a predicted age of the data subject, a predicted age range for the data subject, or an age prediction confidence level for the data subject. In yet another aspects, the consent receipt set comprises a consent status and generating the consent receipt set comprises initially setting the consent status to valid in response to the request to initiate the transaction, and modifying the consent receipt set to prevent the computing system from providing the data subject with access to the functionality requiring valid consent comprises modifying the consent status to invalid in response to determining the data subject does not meet the age criterion.

In some aspects, the piece of identifying information comprises an image of the data subject. In particular aspects, the operations further comprise determining image capture data associated with the image of the data subject, the image capture data comprising at least one of a capture time of the image of the data subject or an image capture location of the image of the data subject. In such aspects, determining whether the data subject satisfies the age criterion may be further based on the image capture data. In various aspects, the image capture data comprises the image capture location of the image, and determining whether the data subject satisfies the age criterion comprises determining whether the image capture location is within a particular distance of a determined location of a computing device from which the request to initiate the transaction originated.

A non-transitory computer-readable medium, according to various aspects, stores computer-executable instructions that, when executed by processing hardware, configure the processing hardware to perform operations comprising responsive to a request to initiate a transaction between an entity and a data subject, generating a consent receipt set. In some aspects, the request comprises a transaction parameter, a data subject parameter, and a consent parameter indicating consent by the data subject to processing of personal data received via a computer network as part of the transaction. In particular aspects, the consent receipt set comprises a consent receipt identifier, a transaction identifier based on the transaction parameter, a consent status based on the consent parameter, and a subject identifier based on the data subject parameter. According to particular aspects, the operations further comprise: (1) receiving an image of the data subject; (2) causing an artificial image system to generate an output related to determining an age of the data subject by providing the image of the data subject to the artificial intelligence image system for analysis; (3) receiving the output related to determining the age of the data subject from the artificial intelligence image system; (4) determining, based on the output, whether the data subject satisfies an age criterion for the transaction; and (5) in response to determining that the data subject does not meet the age criterion, modifying the consent status to prevent a computing system from providing the data subject with access to functionality requiring valid consent.

According to a particular aspect, the output comprises at least one of a predicted age of the data subject, a predicted age range for the data subject, or an age prediction confidence level for the data subject. In some aspects, the output comprises a prediction usable for determining the age of the data subject. In a particular aspect, the output comprises a second prediction usable for determining whether the data subject is performing a particular action in the image of the data subject. In various aspects, the operations further comprise determining image capture data associated with the image of the data subject, the image capture data comprising at least one of a capture time of the image of the data subject or an image capture location of the image of the data subject. In particular aspects, determining whether the data subject satisfies the age criterion is further based on the image capture data.

In a particular aspect, the output comprises a request for additional identifying information. In such aspects, the operations may further comprise: (1) receiving the additional identifying information for the data subject; (2) causing the third-party artificial intelligence system to generate a second output related to determining the age of the data subject by providing the additional identifying information to the third-party artificial intelligence system for analysis; and (3) receiving the second output. In other aspects, determining whether the data subject satisfies the age criterion for the transaction is further based on the second output.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of a data subject access request fulfillment system are described below. In the course of this description, reference will be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

FIG. 1 depicts a data model generation and population system according to particular embodiments.

FIG. 2 is a schematic diagram of a computer (such as the data model generation server 110, or data model population server 120) that is suitable for use in various embodiments of the data model generation and population system shown in FIG. 1 (e.g., or the consent interface management server 6110, or one or more remote computing devices 6150) that is suitable for use in various embodiments of the consent conversion optimization system shown in FIG. 60).

FIG. 3 is a flowchart showing an example of steps performed by a Data Model Generation Module according to particular embodiments.

FIGS. 4-10 depict various exemplary visual representations of data models according to particular embodiments.

FIG. 11 is a flowchart showing an example of steps performed by a Data Model Population Module.

FIG. 12 is a flowchart showing an example of steps performed by a Data Population Questionnaire Generation Module.

FIG. 13 is a process flow for populating a data inventory according to a particular embodiment using one or more data mapping techniques.

FIGS. 14-25 depict exemplary screen displays and graphical user interfaces (GUIs) according to various embodiments of the system, which may display information associated with the system or enable access to, or interaction with, the system by one or more users (e.g., to configure a questionnaire for populating one or more inventory attributes for one or more data models, complete one or more assessments, etc.).

FIG. 26 is a flowchart showing an example of steps performed by an Intelligent Identity Scanning Module.

FIG. 27 is schematic diagram of network architecture for an intelligent identity scanning system 2700 according to a particular embodiment.

FIG. 28 is a schematic diagram of an asset access methodology utilized by an intelligent identity scanning system 2700 in various embodiments of the system.

FIG. 29 is a flowchart showing an example of a processes performed by a Data Subject Access Request Fulfillment Module 2900 according to various embodiments.

FIGS. 30-31 depict exemplary screen displays and graphical user interfaces (GUIs) according to various embodiments of the system, which may display information associated with the system or enable access to, or interaction with, the system by one or more users (e.g., for the purpose of submitting a data subject access request or other suitable request).

FIGS. 32-35 depict exemplary screen displays and graphical user interfaces (GUIs) according to various embodiments of the system, which may display information associated with the system or enable access to, or interaction with, the system by one or more users (e.g., for the purpose of flagging one or more risks associated with one or more particular questionnaire questions).

FIG. 36 depicts a schematic diagram of a centralized data repository system according to particular embodiments of the present system.

FIG. 37 is a flowchart showing an example of a processes performed by a data repository module according to various embodiments, which may, for example, be executed by the centralized data repository system of FIG. 36.

FIG. 38 depicts a schematic diagram of a consent receipt management system according to particular embodiments.

FIGS. 39-54 are computer screen shots that demonstrate the operation of various embodiments.

FIG. 55 depicts an exemplary consent receipt management system according to particular embodiments.

FIG. 56 is a flow chart showing an example of a process performed by a Consent Receipt Management Module 5600 according to particular embodiments.

FIG. 57 is a flow chart showing an example of a process performed by a Consent Expiration and Re-Triggering Module 5700 according to particular embodiments.

FIG. 58 depicts an exemplary screen display and graphical user interface (GUI) according to various embodiments of the system, which may display information associated with the system or enable access to, or interaction with, the system by one or more users (e.g., for the purpose of analyzing one or more consent conversion analytics).

FIG. 59 is a flow chart showing an example of a process performed by a Consent Validity Scoring Module 5900 according to particular embodiments.

FIG. 60 depicts an exemplary consent conversion optimization system according to particular embodiments.

FIG. 61 is a flow chart showing an example of a process performed by a Consent Conversion Optimization Module according to particular embodiments.

FIGS. 62-70 depict exemplary screen displays and graphical user interfaces (GUIs) for enabling a user (e.g., of a particular website) to input consent preferences. These exemplary user interfaces may include, for example, one or more user interfaces that the consent conversion optimization system is configured to test against one another to determine which particular user interface results in a higher rate of consent provided by users.

FIGS. 71-72 depict screen displays that a user may encounter when utilizing one or more system features described herein in various embodiments.

DETAILED DESCRIPTION

Various embodiments now will be described more fully hereinafter with reference to the accompanying drawings. It should be understood that the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout.

Overview

A data model generation and population system, according to particular embodiments, is configured to generate a data model (e.g., one or more data models) that maps one or more relationships between and/or among a plurality of data assets utilized by a corporation or other entity (e.g., individual, organization, etc.) in the context, for example, of one or more business processes. In particular embodiments, each of the plurality of data assets (e.g., data systems) may include, for example, any entity that collects, processes, contains, and/or transfers data (e.g., such as a software application, “internet of things” computerized device, database, web site, data-center, server, etc.). For example, a first data asset may include any software or device (e.g., server or servers) utilized by a particular entity for such data collection, processing, transfer, storage, etc.

As shown in FIGS. 4 and 5, in various embodiments, the data model may store the following information: (1) the organization that owns and/or uses a particular data asset (a primary data asset, which is shown in the center of the data model in FIG. 4); (2) one or more departments within the organization that are responsible for the data asset; (3) one or more software applications that collect data (e.g., personal data) for storage in and/or use by the data asset (e.g., or one or more other suitable collection assets from which the personal data that is collected, processed, stored, etc. by the primary data asset is sourced); (4) one or more particular data subjects (or categories of data subjects) that information is collected from for use by the data asset; (5) one or more particular types of data that are collected by each of the particular applications for storage in and/or use by the data asset; (6) one or more individuals (e.g., particular individuals or types of individuals) that are permitted to access and/or use the data stored in, or used by, the data asset; (7) which particular types of data each of those individuals are allowed to access and use; and (8) one or more data assets (destination assets) that the data is transferred to for other use, and which particular data is transferred to each of those data assets. As shown in FIGS. 6 and 7, the system may also optionally store information regarding, for example, which business processes and processing activities utilize the data asset.

In particular embodiments, the data model stores this information for each of a plurality of different data assets and may include links between, for example, a portion of the model that provides information for a first particular data asset and a second portion of the model that provides information for a second particular data asset.

In various embodiments, the data model generation and population system may be implemented in the context of any suitable privacy management system that is configured to ensure compliance with one or more legal or industry standards related to the collection and/or storage of private information. In various embodiments, a particular organization, sub-group, or other entity may initiate a privacy campaign or other activity (e.g., processing activity) as part of its business activities. In such embodiments, the privacy campaign may include any undertaking by a particular organization (e.g., such as a project or other activity) that includes the collection, entry, and/or storage (e.g., in memory) of any personal data associated with one or more individuals. In particular embodiments, a privacy campaign may include any project undertaken by an organization that includes the use of personal data, or any other activity that could have an impact on the privacy of one or more individuals.

In any embodiment described herein, personal data may include, for example: (1) the name of a particular data subject (which may be a particular individual); (2) the data subject's address; (3) the data subject's telephone number; (4) the data subject's e-mail address; (5) the data subject's social security number; (6) information associated with one or more of the data subject's credit accounts (e.g., credit card numbers); (7) banking information for the data subject; (8) location data for the data subject (e.g., their present or past location); (9) internet search history for the data subject; and/or (10) any other suitable personal information, such as other personal information discussed herein. In particular embodiments, such personal data may include one or more cookies (e.g., where the individual is directly identifiable or may be identifiable based at least in part on information stored in the one or more cookies).

In particular embodiments, when generating a data model, the system may, for example: (1) identify one or more data assets associated with a particular organization; (2) generate a data inventory for each of the one or more data assets, where the data inventory comprises information such as: (a) one or more processing activities associated with each of the one or more data assets, (b) transfer data associated with each of the one or more data assets (data regarding which data is transferred to/from each of the data assets, and which data assets, or individuals, the data is received from and/or transferred to, (c) personal data associated with each of the one or more data assets (e.g., particular types of data collected, stored, processed, etc. by the one or more data assets), and/or (d) any other suitable information; and (3) populate the data model using one or more suitable techniques.

In particular embodiments, the one or more techniques for populating the data model may include, for example: (1) obtaining information for the data model by using one or more questionnaires associated with a particular privacy campaign, processing activity, etc.; (2) using one or more intelligent identity scanning techniques discussed herein to identify personal data stored by the system and map such data to a suitable data model, data asset within a data model, etc.; (3) obtaining information for the data model from a third-party application (or other application) using one or more application programming interfaces (API); and/or (4) using any other suitable technique.

In particular embodiments, the system is configured to generate and populate a data model substantially on the fly (e.g., as the system receives new data associated with particular processing activities). In still any embodiment described herein, the system is configured to generate and populate a data model based at least in part on existing information stored by the system (e.g., in one or more data assets), for example, using one or more suitable scanning techniques described herein.

As may be understood in light of this disclosure, a particular organization may undertake a plurality of different privacy campaigns, processing activities, etc. that involve the collection and storage of personal data. In some embodiments, each of the plurality of different processing activities may collect redundant data (e.g., may collect the same personal data for a particular individual more than once), and may store data and/or redundant data in one or more particular locations (e.g., on one or more different servers, in one or more different databases, etc.). In this way, a particular organization may store personal data in a plurality of different locations which may include one or more known and/or unknown locations. By generating and populating a data model of one or more data assets that are involved in the collection, storage and processing of such personal data, the system may be configured to create a data model that facilitates a straightforward retrieval of information stored by the organization as desired. For example, in various embodiments, the system may be configured to use a data model in substantially automatically responding to one or more data access requests by an individual (e.g., or other organization). Various embodiments of a system for generating and populating a data model are described more fully below.

In particular embodiments, any entity (e.g., organization, company, etc.) that collects, stores, processes, etc. personal data may require one or more of: (1) consent from a data subject from whom the personal data is collected and/or processed; and/or (2) a lawful basis for the collection and/or processing of the personal data. In various embodiments, the entity may be required to, for example: (1) demonstrate that a data subject has freely given specific, informed, and unambiguous indication of the data subject's agreement to the processing of his or her personal data (e.g., in the form of a statement or clear affirmative action); (2) demonstrate that the entity received consent from a data subject in a manner clearly distinguishable from other matters (e.g., in an intelligible and easily accessible form, using clear and plain language, etc.); (3) enable a data subject to withdraw consent as easily as the data subject can give consent; (4) separate a data subject's consent from performance under any contract unless such processing is necessary for performance under the contract; etc.

In various embodiments, a consent receipt management system may be implemented in the context of any suitable privacy management system that is configured to ensure compliance with one or more legal or industry standards related to the collection and/or storage of private information (e.g., such as personal data). Various privacy and security policies (e.g., such as the European Union's General Data Protection Regulation, California's California Consumer Privacy Act, and other such policies) may provide data subjects (e.g., individuals, organizations, or other entities) with certain rights related to the data subject's personal data that is collected, stored, or otherwise processed by an organization. These rights may include, for example: (1) a right to erasure of the data subject's personal data (e.g., in cases where no legal basis applies to the processing and/or collection of the personal data; (2) a right to withdraw consent to the processing and/or collection of their personal data; (3) a right to receive the personal data concerning the data subject, which he or she has provided to an entity (e.g., organization), in a structured, commonly used and machine-readable format; and/or (4) any other right which may be afforded to the data subject under any applicable legal and/or industry policy.

In particular embodiments, the consent receipt management system is configured to: (1) enable an entity to demonstrate that valid consent has been obtained for each particular data subject for whom the entity collects and/or processes personal data; and (2) enable one or more data subjects to exercise one or more rights described herein.

The system may, for example, be configured to track data on behalf of an entity that collects and/or processes personal data related to: (1) who consented to the processing or collection of personal data (e.g., the data subject themselves or a person legally entitled to consent on their behalf such as a parent, guardian, etc.); (2) when the consent was given (e.g., a date and time); (3) what information was provided to the consenter at the time of consent (e.g., a privacy policy, what personal data would be collected following the provision of the consent, for what purpose that personal data would be collected, etc.); (4) how consent was received (e.g., one or more copies of a data capture form, web form, etc. via which consent was provided by the consenter); (5) when consent was withdrawn (e.g., a date and time of consent withdrawal if the consenter withdraws consent); and/or (6) any other suitable data related to receipt or withdrawal of consent. In particular embodiments, the system is configured to store metadata in association with processed personal data that indicates one or more pieces of consent data that authorized the processing of the personal data.

In further embodiments, the system may be configured to provide data subjects with a centralized interface that is configured to: (1) provide information regarding each of one or more valid consents that the data subject has provided to one or more entities related to the collection and/or processing of their personal data; (2) provide one or more periodic reminders regarding the data subject's right to withdraw previously given consent (e.g., every 6 months in the case of communications data and metadata, etc.); (3) provide a withdrawal mechanism for the withdrawal of one or more previously provided valid consents (e.g., in a format that is substantially similar to a format in which the valid consent was given by the data subject); (4) refresh consent when appropriate (e.g., the system may be configured to elicit updated consent in cases where particular previously validly consented to processing is used for a new purpose, a particular amount of time has elapsed since consent was given, etc.).

In particular embodiments, the system is configured to manage one or more consent receipts between a data subject and an entity. In various embodiments, a consent receipt may include a record (e.g., a data record stored in memory and associated with the data subject) of consent, for example, as a transactional agreement where the data subject is already identified or identifiable as part of the data processing that results from the provided consent. In any embodiment described herein, the system may be configured to generate a consent receipt in response to a data subject providing valid consent. In some embodiments, the system is configured to determine whether one or more conditions for valid consent have been met prior to generating the consent receipt. Various embodiments of a consent receipt management system are described more fully below.

In particular embodiments, any entity (e.g., organization, company, etc.) that collects, stores, processes, etc. personal data may require one or more of: (1) consent from a data subject from whom the personal data is collected and/or processed; and/or (2) a lawful basis for the collection and/or processing of the personal data. In various embodiments, the entity may be required to, for example: (1) demonstrate that a data subject has freely given specific, informed, and unambiguous indication of the data subject's agreement to the processing of his or her personal data (e.g., in the form of a statement or clear affirmative action); (2) demonstrate that the entity received consent from a data subject in a manner clearly distinguishable from other matters (e.g., in an intelligible and easily accessible form, using clear and plain language, etc.); (3) enable a data subject to withdraw consent as easily as the data subject can give consent; (4) separate a data subject's consent from performance under any contract unless such processing is necessary for performance under the contract; etc.

In particular, when storing or retrieving information from an end user's device, an entity may be required to receive consent from the end user for such storage and retrieval. Web cookies are a common technology that may be directly impacted by the consent requirements discussed herein. Accordingly, an entity that use cookies (e.g., on one or more webpages) may be required to use one or more banners, pop-ups or other user interfaces on the website in order to capture consent from end-users to store and retrieve cookie data.

The consent required to store and retrieve cookie data may, for example, require a clear affirmative act establishing a freely given, specific, informed and unambiguous indication of a data subject's agreement to the processing of personal data. This may include, ticking a box when visiting an internet website, choosing technical settings for information society services, or any other suitable statement or conduct which clearly indicates in this context the data subject's acceptance of the proposed processing of their personal data.

In various embodiments, pre-ticked boxes (or other preselected options) or inactivity may not be sufficient to demonstrate freely given consent. For example, an entity may be unable to rely on implied consent (e.g., “by visiting this website, you accept cookies”). Without a genuine and free choice by data subjects and/or other end users, an entity may be unable to demonstrate valid consent (e.g., and therefore unable to utilize cookies in association with such data subjects and/or end users).

A particular entity may use cookies for any number of suitable reasons. For example, an entity may utilize: (1) one or more functionality cookies (which may, for example, enhance the functionality of a website by storing user preferences such as location for a weather or news website); (2) one or more performance cookies (which may, for example, help to improve performance of the website on the user's device to provide a better user experience); (3) one or more targeting cookies (which may, for example, be used by advertising partners to build a profile of interests for a user in order to show relevant advertisements through the website; (4) etc. Cookies may also be used for any other suitable reason such as, for example: (1) to measure and improve site quality through analysis of visitor behavior (e.g., through ‘analytics’); (2) to personalize pages and remember visitor preferences; (3) to manage shopping carts in online stores; (4) to track people across websites and deliver targeted advertising; (5) etc.

Under various regulations, an entity may not be required to obtain consent to use every type of cookie utilized by a particular website. For example, strictly necessary cookies, which may include cookies that are necessary for a website to function, may not require consent. An example of strictly necessary cookies may include, for example, session cookies. Session cookies may include cookies that are strictly required for website functionality and don't track user activity once the browser window is closed. Examples of session cookies include: (1) faceted search filter cookies; (2) user authentication cookies; (3) cookies that enable shopping cart functionality; (4) cookies used to enable playback of multimedia content; (5) etc.

Cookies which may trigger a requirement for obtaining consent may include cookies such as persistent cookies. Persistent cookies may include, for example, cookies used to track user behavior even after the use has moved on from a website or closed a browser window.

In order to comply with particular regulations, an entity may be required to: (1) present visitors with information about the cookies a website uses and the purpose of the cookies (e.g., any suitable purpose described herein or other suitable purpose); (2) obtain consent to use those cookies (e.g., obtain separate consent to use each particular type of cookies used by the web site); and (3) provide a mechanism for visitors to withdraw consent (e.g., that is as straightforward as the mechanism through which the visitors initially provided consent). In any embodiment described herein, an entity may only need to receive valid consent from any particular visitor a single time (e.g., returning visitors may not be required to provide consent on subsequent visits to the site). In particular embodiments, although they may not require explicit consent to use, an entity may be required to notify a visitor of any strictly necessary cookies used by a website.

Because entities may desire to maximize a number of end users and other data subjects that provide this valid consent, it may be beneficial to provide a user interface through which the users are more likely to provide such consent. By receiving consent from a high number of users, the entity may, for example: (1) receive higher revenue from advertising partners; (2) receive more traffic to the website because users of the website may enjoy a better experience while visiting the website; etc.

In particular embodiments, a consent conversion optimization system is configured to test two or more test consent interfaces against one another to determine which of the two or more consent interfaces results in a higher conversion percentage (e.g., to determine which of the two or more interfaces lead to a higher number of end users and/or data subjects providing a requested level of consent for the creation, storage and use or cookies by a particular website). The system may, for example, analyze end user interaction with each particular test consent interface to determine which of the two or more user interfaces: (1) result in a higher incidence of a desired level of provided consent; (2) are easier to use by the end users and/or data subjects (e.g., take less time to complete, require a fewer number of clicks, etc.); (3) etc.

The system may then be configured to automatically select from between/among the two or more test interfaces and use the selected interface for future visitors of the website.

In particular embodiments, the system is configured to test the two or more test consent interfaces against one another by: (1) presenting a first test interface of the two or more test consent interfaces to a first portion of visitors to a website; (2) collecting first consent data from the first portion of visitors based on the first test interface; (3) presenting a second test interface of the two or more test consent interfaces to a second portion of visitors to the website; (4) collecting second consent data from the second portion of visitors based on the second test interface; (5) analyzing and comparing the first consent data and second consent data to determine which of the first and second test interface results in a higher incidence of desired consent; and (6) selecting between the first and second test interface based on the analysis.

In particular embodiments, the system is configured to enable a user to select a different template for each particular test interface. In any embodiment described herein, the system is configured to automatically select from a plurality of available templates when performing testing. In still any embodiment described herein, the system is configured to select one or more interfaces for testing based on similar analysis performed for one or more other websites.

In still any embodiment described herein, the system is configured to use one or more additional performance metrics when testing particular cookie consent interfaces (e.g., against one another). The one or more additional performance metrics may include, for example: (1) opt-in percentage (e.g., a percentage of users that click the ‘accept all’ button on a cookie consent test banner; (2) average time-to-interaction (e.g., an average time that users wait before interacting with a particular test banner); (3) average time-to-site (e.g., an average time that it takes a user to proceed to normal navigation across an entity site after interacting with the cookie consent test banner; (4) dismiss percentage (e.g., a percentage of users that dismiss the cookie consent banner using the close button, by scrolling, or by clicking on grayed-out website); (5) functional cookies only percentage (e.g., a percentage of users that opt out of any cookies other than strictly necessary cookies); (6) performance opt-out percentage; (7) targeting opt-out percentage; (8) social opt-out percentage; (9) etc.

Various embodiments of a consent conversion optimization system are described more fully below.

In particular embodiments, an automated process blocking system is configured to substantially automatically block one or more processes (e.g., one or more data processing processes) based on received user consent data. For example, as may be understood in light of this disclosure, a particular data subject may provide consent for an entity to process particular data associated with the data subject for one or more particular purposes. In any embodiment of the system described herein, the system may be configured to: (1) receive an indication that one or more entity systems are processing one or more pieces of personal data associated with a particular data subject; (2) in response to receiving the indication, identifying at least one process for which the one or more pieces of personal data are being processed; (3) determine, using a consent receipt management system, whether the data subject has provided valid consent for the processing of the one or more pieces of personal data for the at least one process; (4) at least partially in response to determining that the data subject has not provided valid consent for the processing of the one or more pieces of personal data for the at least one process, automatically blocking the processing.

In particular embodiments, a consent receipt management system is configured to provide a centralized repository of consent receipt preferences for a plurality of data subjects. In various embodiments, the system is configured to provide an interface to the plurality of data subjects for modifying consent preferences and capture consent preference changes. The system may provide the ability to track the consent status of pending and confirmed consents. In other embodiments, the system may provide a centralized repository of consent receipts that a third-party system may reference when taking one or more actions related to a processing activity. For example, a particular entity may provide a newsletter that one or more data subjects have consented to receiving. Each of the one or more data subjects may have different preferences related to how frequently they would like to receive the newsletter, etc. In particular embodiments, the consent receipt management system may receive a request form a third-party system to transmit the newsletter to the plurality of data subjects. The system may then cross-reference an updated consent database to determine which of the data subjects have a current consent to receive the newsletter, and whether transmitting the newsletter would conflict with any of those data subjects' particular frequency preferences. The system may then be configured to transmit the newsletter to the appropriate identified data subjects.

In various embodiments, the system may be configured to: (1) determine whether there is a legal basis for processing of particular data prior to processing the data; (2) in response to determining that there is a legal basis, allowing the processing and generating a record for the processing that includes one or more pieces of evidence demonstrating the legal basis (e.g., the user has consented, the processing is strictly necessary, etc.); and (3) in response to determining that there is no legal basis, blocking the processing from occurring. In particular embodiments, the system may be embodied as a processing permission engine, which may, for example, interface with a consent receipt management system. The system may, for example, be configured to access the consent receipt management system to determine whether an entity is able to process particular data for particular data subjects (e.g., for one or more particular purposes). In particular embodiments, one or more entity computer system may be configured to interface with one or more third party central consent data repositories prior to processing data (e.g., to determine whether the entity has consent or some other legal basis for processing the data).

In particular other embodiments, the system is configured to perform one or more risk analyses related to the processing in addition to identifying whether the entity has consent or some other legal basis. The system may analyze the risk of the processing based on, for example: (1) a purpose of the processing; (2) a type of data being processed; and/or (3) any other suitable factor. In particular embodiments, the system is configured to determine whether to continue with the processing based on a combination of identifying a legal basis for the processing and the risk analysis. For example, the system may determine that there is a legal basis to process the data, but that the processing is particularly risky. In this example, the system may determine to block the processing of the data despite the legal basis because of the determined risk level. The risk analysis may be further based on, for example, a risk tolerance of the entity/organization, or any other suitable factor.

Various embodiments of an automated process blocking system are described more fully below.

Exemplary Technical Platforms

As will be appreciated by one skilled in the relevant field, the present invention may be, for example, embodied as a computer system, a method, or a computer program product. Accordingly, various embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, particular embodiments may take the form of a computer program product stored on a computer-readable storage medium having computer-readable instructions (e.g., software) embodied in the storage medium. Various embodiments may take the form of web-implemented computer software. Any suitable computer-readable storage medium may be utilized including, for example, hard disks, compact disks, DVDs, optical storage devices, and/or magnetic storage devices.

Various embodiments are described below with reference to block diagrams and flowchart illustrations of methods, apparatuses (e.g., systems), and computer program products. It should be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by a computer executing computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus to create means for implementing the functions specified in the flowchart block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner such that the instructions stored in the computer-readable memory produce an article of manufacture that is configured for implementing the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrations support combinations of mechanisms for performing the specified functions, combinations of steps for performing the specified functions, and program instructions for performing the specified functions. It should also be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, can be implemented by special purpose hardware-based computer systems that perform the specified functions or steps, or combinations of special purpose hardware and other hardware executing appropriate computer instructions.

Example System Architecture

FIG. 1 is a block diagram of a Data Model Generation and Population System 100 according to a particular embodiment. In various embodiments, the Data Model Generation and Population System 100 is part of a privacy compliance system (also referred to as a privacy management system), or other system, which may, for example, be associated with a particular organization and be configured to aid in compliance with one or more legal or industry regulations related to the collection and storage of personal data. In some embodiments, the Data Model Generation and Population System 100 is configured to: (1) generate a data model based on one or more identified data assets, where the data model includes a data inventory associated with each of the one or more identified data assets; (2) identify populated and unpopulated aspects of each data inventory; and (3) populate the unpopulated aspects of each data inventory using one or more techniques such as intelligent identity scanning, questionnaire response mapping, APIs, etc.

As may be understood from FIG. 1, the Data Model Generation and Population System 100 includes one or more computer networks 115, a Data Model Generation Server 110, a Data Model Population Server 120, an Intelligent Identity Scanning Server 130, One or More Databases 140 or other data structures, one or more remote computing devices 150 (e.g., a desktop computer, laptop computer, tablet computer, smartphone, etc.), and One or More Third Party Servers 160. In particular embodiments, the one or more computer networks 115 facilitate communication between the Data Model Generation Server 110, Data Model Population Server 120, Intelligent Identity Scanning Server 130, One or More Databases 140, one or more remote computing devices 150 (e.g., a desktop computer, laptop computer, tablet computer, smartphone, etc.), and One or More Third Party Servers 160. Although in the embodiment shown in FIG. 1, the Data Model Generation Server 110, Data Model Population Server 120, Intelligent Identity Scanning Server 130, One or More Databases 140, one or more remote computing devices 150 (e.g., a desktop computer, laptop computer, tablet computer, smartphone, etc.), and One or More Third Party Servers 160 are shown as separate servers, it should be understood that in any embodiment described herein, one or more of these servers and/or computing devices may comprise a single server, a plurality of servers, one or more cloud-based servers, or any other suitable configuration.

The one or more computer networks 115 may include any of a variety of types of wired or wireless computer networks such as the Internet, a private intranet, a public switch telephone network (PSTN), or any other type of network. The communication link between The Intelligent Identity Scanning Server 130 and the One or More Third Party Servers 160 may be, for example, implemented via a Local Area Network (LAN) or via the Internet. In any embodiment described herein, the One or More Databases 140 may be stored either fully or partially on any suitable server or combination of servers described herein.

FIG. 2 illustrates a diagrammatic representation of a computer 200 that can be used within the Data Model Generation and Population System 100, for example, as a client computer (e.g., one or more remote computing devices 130 shown in FIG. 1), or as a server computer (e.g., Data Model Generation Server 110 shown in FIG. 1). In particular embodiments, the computer 200 may be suitable for use as a computer within the context of the Data Model Generation and Population System 100 that is configured to generate a data model and map one or more relationships between one or more pieces of data that make up the model.

In particular embodiments, the computer 200 may be connected (e.g., networked) to other computers in a LAN, an intranet, an extranet, and/or the Internet. As noted above, the computer 200 may operate in the capacity of a server or a client computer in a client-server network environment, or as a peer computer in a peer-to-peer (or distributed) network environment. The Computer 200 may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, a switch or bridge, or any other computer capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that computer. Further, while only a single computer is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

An exemplary computer 200 includes a processing device 202, a main memory 204 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), static memory 206 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 218, which communicate with each other via a bus 232.

The processing device 202 represents one or more general-purpose processing devices such as a microprocessor, a central processing unit, or the like. More particularly, the processing device 202 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or processor implementing other instruction sets, or processors implementing a combination of instruction sets. The processing device 202 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 202 may be configured to execute processing logic 226 for performing various operations and steps discussed herein.

The computer 120 may further include a network interface device 208. The computer 200 also may include a video display unit 210 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 212 (e.g., a keyboard), a cursor control device 214 (e.g., a mouse), and a signal generation device 216 (e.g., a speaker).

The data storage device 218 may include a non-transitory computer-accessible storage medium 230 (also known as a non-transitory computer-readable storage medium or a non-transitory computer-readable medium) on which is stored one or more sets of instructions (e.g., software instructions 222) embodying any one or more of the methodologies or functions described herein. The software instructions 222 may also reside, completely or at least partially, within main memory 204 and/or within processing device 202 during execution thereof by computer 200—main memory 204 and processing device 202 also constituting computer-accessible storage media. The software instructions 222 may further be transmitted or received over a network 115 via network interface device 208.

While the computer-accessible storage medium 230 is shown in an exemplary embodiment to be a single medium, the term “computer-accessible storage medium” should be understood to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-accessible storage medium” should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the computer and that cause the computer to perform any one or more of the methodologies of the present invention. The term “computer-accessible storage medium” should accordingly be understood to include, but not be limited to, solid-state memories, optical and magnetic media, etc.

Exemplary System Platform

Various embodiments of a Data Model Generation and Population System 100 may be implemented in the context of any suitable system (e.g., a privacy compliance system). For example, the Data Model Generation and Population System 100 may be implemented to analyze a particular company or other organization's data assets to generate a data model for one or more processing activities, privacy campaigns, etc. undertaken by the organization. In particular embodiments, the system may implement one or more modules in order to at least partially ensure compliance with one or more regulations (e.g., legal requirements) related to the collection and/or storage of personal data. Various aspects of the system's functionality may be executed by certain system modules, including a Data Model Generation Module 300, Data Model Population Module 1100, Data Population Questionnaire Generation Module 1200, Intelligent Identity Scanning Module 2600, and Data Subject Access Request Fulfillment Module 2900. These modules are discussed in greater detail below.

Although these modules are presented as a series of steps, it should be understood in light of this disclosure that various embodiments of the Data Model Generation Module 300, Data Model Population Module 1100, Data Population Questionnaire Generation Module 1200, Intelligent Identity Scanning Module 2600, and Data Subject Access Request Fulfillment Module 2900 described herein may perform the steps described below in an order other than in which they are presented. In still any embodiment described herein, the Data Model Generation Module 300, Data Model Population Module 1100, Data Population Questionnaire Generation Module 1200, Intelligent Identity Scanning Module 2600, and Data Subject Access Request Fulfillment Module 2900 may omit certain steps described below. In any embodiment described herein, the Data Model Generation Module 300, Data Model Population Module 1100, Data Population Questionnaire Generation Module 1200, Intelligent Identity Scanning Module 2600, and Data Subject Access Request Fulfillment Module 2900 may perform steps in addition to those described (e.g., such as one or more steps described with respect to one or more other modules, etc.).

Data Model Generation Module

In particular embodiments, a Data Model Generation Module 300 is configured to: (1) generate a data model (e.g., a data inventory) for one or more data assets utilized by a particular organization; (2) generate a respective data inventory for each of the one or more data assets; and (3) map one or more relationships between one or more aspects of the data inventory, the one or more data assets, etc. within the data model. In particular embodiments, a data asset (e.g., data system, software application, etc.) may include, for example, any entity that collects, processes, contains, and/or transfers data (e.g., such as a software application, “internet of things” computerized device, database, website, data-center, server, etc.). For example, a first data asset may include any software or device (e.g., server or servers) utilized by a particular entity for such data collection, processing, transfer, storage, etc.

In particular embodiments, a particular data asset, or collection of data assets, may be utilized as part of a particular data processing activity (e.g., direct deposit generation for payroll purposes). In various embodiments, a data model generation system may, on behalf of a particular organization (e.g., entity), generate a data model that encompasses a plurality of processing activities. In any embodiment described herein, the system may be configured to generate a discrete data model for each of a plurality of processing activities undertaken by an organization.

Turning to FIG. 3, in particular embodiments, when executing the Data Model Generation Module 300, the system begins, at Step 310, by generating a data model for one or more data assets and digitally storing the data model in computer memory. The system may, for example, store the data model in the One or More Databases 140 described above (or any other suitable data structure). In various embodiments, generating the data model comprises generating a data structure that comprises information regarding one or more data assets, attributes and other elements that make up the data model. As may be understood in light of this disclosure, the one or more data assets may include any data assets that may be related to one another. In particular embodiments, the one or more data assets may be related by virtue of being associated with a particular entity (e.g., organization). For example, the one or more data assets may include one or more computer servers owned, operated, or utilized by the entity that at least temporarily store data sent, received, or otherwise processed by the particular entity.

In still any embodiment described herein, the one or more data assets may comprise one or more third party assets which may, for example, send, receive and/or process personal data on behalf of the particular entity. These one or more data assets may include, for example, one or more software applications (e.g., such as Expensify to collect expense information, QuickBooks to maintain and store salary information, etc.).

Continuing to step 320, the system is configured to identify a first data asset of the one or more data assets. In particular embodiments, the first data asset may include, for example, any entity (e.g., system) that collects, processes, contains, and/or transfers data (e.g., such as a software application, “internet of things” computerized device, database, website, data-center, server, etc.). For example, the first data asset may include any software or device utilized by a particular organization for such data collection, processing, transfer, etc. In various embodiments, the first data asset may be associated with a particular processing activity (e.g., the first data asset may make up at least a part of a data flow that relates to the collection, storage, transfer, access, use, etc. of a particular piece of data (e.g., personal data)). Information regarding the first data asset may clarify, for example, one or more relationships between and/or among one or more other data assets within a particular organization. In a particular example, the first data asset may include a software application provided by a third party (e.g., a third party vendor) with which the particular entity interfaces for the purpose of collecting, storing, or otherwise processing personal data (e.g., personal data regarding customers, employees, potential customers, etc.).

In particular embodiments, the first data asset is a storage asset that may, for example: (1) receive one or more pieces of personal data form one or more collection assets; (2) transfer one or more pieces of personal data to one or more transfer assets; and/or (3) provide access to one or more pieces of personal data to one or more authorized individuals (e.g., one or more employees, managers, or other authorized individuals within a particular entity or organization). In a particular embodiment, the first data asset is a primary data asset associated with a particular processing activity around which the system is configured to build a data model associated with the particular processing activity.

In particular embodiments, the system is configured to identify the first data asset by scanning a plurality of computer systems associated with a particular entity (e.g., owned, operated, utilized, etc. by the particular entity). In various embodiments, the system is configured to identify the first data asset from a plurality of data assets identified in response to completion, by one or more users, of one or more questionnaires.

Advancing to Step 330, the system generates a first data inventory of the first data asset. The data inventory may comprise, for example, one or more inventory attributes associated with the first data asset such as, for example: (1) one or more processing activities associated with the first data asset; (2) transfer data associated with the first data asset (e.g., how and where the data is being transferred to and/or from); (3) personal data associated with the first data asset (e.g., what type of personal data is collected and/or stored by the first data asset; how, and from where, the data is collected, etc.); (4) storage data associated with the personal data (e.g., whether the data is being stored, protected and deleted); and (5) any other suitable attribute related to the collection, use, and transfer of personal data. In any embodiment described herein, the one or more inventory attributes may comprise one or more other pieces of information such as, for example: (1) the type of data being stored by the first data asset; (2) an amount of data stored by the first data asset; (3) whether the data is encrypted; (4) a location of the stored data (e.g., a physical location of one or more computer servers on which the data is stored); etc. In particular any embodiment described herein, the one or more inventory attributes may comprise one or more pieces of information technology data related to the first data asset (e.g., such as one or more pieces of network and/or infrastructure information, IP address, MAC address, etc.).

In various embodiments, the system may generate the data inventory based at least in part on the type of first data asset. For example, particular types of data assets may have particular default inventory attributes. In such embodiments, the system is configured to generate the data inventory for the first data asset, which may, for example, include one or more placeholder fields to be populated by the system at a later time. In this way, the system may, for example, identify particular inventory attributes for a particular data asset for which information and/or population of data is required as the system builds the data model.

As may be understood in light of this disclosure, the system may, when generating the data inventory for the first data asset, generate one or more placeholder fields that may include, for example: (1) the organization (e.g., entity) that owns and/or uses the first data asset (a primary data asset, which is shown in the center of the data model in FIG. 4); (2) one or more departments within the organization that are responsible for the first data asset; (3) one or more software applications that collect data (e.g., personal data) for storage in and/or use by the first data asset (e.g., or one or more other suitable collection assets from which the personal data that is collected, processed, stored, etc. by the first data asset is sourced); (4) one or more particular data subjects (or categories of data subjects) that information is collected from for use by the first data asset; (5) one or more particular types of data that are collected by each of the particular applications for storage in and/or use by the first data asset; (6) one or more individuals (e.g., particular individuals or types of individuals) that are permitted to access and/or use the data stored in, or used by, the first data asset; (7) which particular types of data each of those individuals are allowed to access and use; and (8) one or more data assets (destination assets) that the data is transferred to from the first data asset, and which particular data is transferred to each of those data assets.

As may be understood in light of this disclosure, the system may be configured to generate the one or more placeholder fields based at least in part on, for example: (1) the type of the first data asset; (2) one or more third party vendors utilized by the particular organization; (3) a number of collection or storage assets typically associated with the type of the first data asset; and/or (4) any other suitable factor related to the first data asset, its one or more inventory attributes, etc. In any embodiment described herein, the system may substantially automatically generate the one or more placeholders based at least in part on a hierarchy and/or organization of the entity for which the data model is being built. For example, a particular entity may have a marketing division, legal department, human resources department, engineering division, or other suitable combination of departments that make up an overall organization. Other particular entities may have further subdivisions within the organization. When generating the data inventory for the first data asset, the system may identify that the first data asset will have both an associated organization and subdivision within the organization to which it is assigned. In this example, the system may be configured to store an indication in computer memory that the first data asset is associated with an organization and a department within the organization.

Next, at Step 340, the system modifies the data model to include the first data inventory and electronically links the first data inventory to the first data asset within the data model. In various embodiments, modifying the data model may include configuring the data model to store the data inventory in computer memory, and to digitally associate the data inventory with the first data asset in memory.

FIGS. 4 and 5 show a data model according to a particular embodiment. As shown in these figures, the data model may store the following information for the first data asset: (1) the organization that owns and/or uses the first data asset; (2) one or more departments within the organization that are responsible for the first data asset; (3) one or more applications that collect data (e.g., personal data) for storage in and/or use by the first data asset; (4) one or more particular data subjects that information is collected from for use by the first data asset; (5) one or more collection assets from which the first asset receives data (e.g., personal data); (6) one or more particular types of data that are collected by each of the particular applications (e.g., collection assets) for storage in and/or use by the first data asset; (7) one or more individuals (e.g., particular individuals, types of individuals, or other parties) that are permitted to access and/or use the data stored in or used by the first data asset; (8) which particular types of data each of those individuals are allowed to access and use; and (9) one or more data assets (destination assets) the data is transferred to for other use, and which particular data is transferred to each of those data assets. As shown in FIGS. 6 and 7, the system may also optionally store information regarding, for example, which business processes and processing activities utilize the first data asset.

As noted above, in particular embodiments, the data model stores this information for each of a plurality of different data assets and may include one or more links between, for example, a portion of the model that provides information for a first particular data asset and a second portion of the model that provides information for a second particular data asset.

Advancing to Step 350, the system next identifies a second data asset from the one or more data assets. In various embodiments, the second data asset may include one of the one or more inventory attributes associated with the first data asset (e.g., the second data asset may include a collection asset associated with the first data asset, a destination asset or transfer asset associated with the first data asset, etc.). In various embodiments, as may be understood in light of the exemplary data models described below, a second data asset may be a primary data asset for a second processing activity, while the first data asset is the primary data asset for a first processing activity. In such embodiments, the second data asset may be a destination asset for the first data asset as part of the first processing activity. The second data asset may then be associated with one or more second destination assets to which the second data asset transfers data. In this way, particular data assets that make up the data model may define one or more connections that the data model is configured to map and store in memory.

Returning to Step 360, the system is configured to identify one or more attributes associated with the second data asset, modify the data model to include the one or more attributes, and map the one or more attributes of the second data asset within the data model. The system may, for example, generate a second data inventory for the second data asset that comprises any suitable attribute described with respect to the first data asset above. The system may then modify the data model to include the one or more attributes and store the modified data model in memory. The system may further, in various embodiments, associate the first and second data assets in memory as part of the data model. In such embodiments, the system may be configured to electronically link the first data asset with the second data asset. In various embodiments, such association may indicate a relationship between the first and second data assets in the context of the overall data model (e.g., because the first data asset may serve as a collection asset for the second data asset, etc.).

Next, at Step 370, the system may be further configured to generate a visual representation of the data model. In particular embodiments, the visual representation of the data model comprises a data map. The visual representation may, for example, include the one or more data assets, one or more connections between the one or more data assets, the one or more inventory attributes, etc.

In particular embodiments, generating the visual representation (e.g., visual data map) of a particular data model (e.g., data inventory) may include, for example, generating a visual representation that includes: (1) a visual indication of a first data asset (e.g., a storage asset), a second data asset (e.g., a collection asset), and a third data asset (e.g., a transfer asset); (2) a visual indication of a flow of data (e.g., personal data) from the second data asset to the first data asset (e.g., from the collection asset to the storage asset); (3) a visual indication of a flow of data (e.g., personal data) from the first data asset to the third data asset (e.g., from the storage asset to the transfer asset); (4) one or more visual indications of a risk level associated with the transfer of personal data; and/or (5) any other suitable information related to the one or more data assets, the transfer of data between/among the one or more data assets, access to data stored or collected by the one or more data assets, etc.

In particular embodiments, the visual indication of a particular asset may comprise a box, symbol, shape, or other suitable visual indicator. In particular embodiments, the visual indication may comprise one or more labels (e.g., a name of each particular data asset, a type of the asset, etc.). In still any embodiment described herein, the visual indication of a flow of data may comprise one or more arrows. In particular embodiments, the visual representation of the data model may comprise a data flow, flowchart, or other suitable visual representation.

In various embodiments, the system is configured to display (e.g., to a user) the generated visual representation of the data model on a suitable display device.

Exemplary Data Models and Visual Representations of Data Models (e.g., Data Maps)

FIGS. 4-10 depict exemplary data models according to various embodiments of the system described herein. FIG. 4, for example, depicts an exemplary data model that does not include a particular processing activity (e.g., that is not associated with a particular processing activity). As may be understood from the data model shown in this figure, a particular data asset (e.g., a primary data asset) may be associated with a particular company (e.g., organization), or organization within a particular company, sub-organization of a particular organization, etc. In still any embodiment described herein, the particular asset may be associated with one or more collection assets (e.g., one or more data subjects from whom personal data is collected for storage by the particular asset), one or more parties that have access to data stored by the particular asset, one or more transfer assets (e.g., one or more assets to which data stored by the particular asset may be transferred), etc.

As may be understood from FIG. 4, a particular data model for a particular asset may include a plurality of data elements. When generating the data model for the particular asset, a system may be configured to substantially automatically identify one or more types of data elements for inclusion in the data model, and automatically generate a data model that includes those identified data elements (e.g., even if one or more of those data elements must remain unpopulated because the system may not initially have access to a value for the particular data element). In such cases, the system may be configured to store a placeholder for a particular data element until the system is able to populate the particular data element with accurate data.

As may be further understood from FIG. 4, the data model shown in FIG. 4 may represent a portion of an overall data model. For example, in the embodiment shown in this figure, the transfer asset depicted may serve as a storage asset for another portion of the data model. In such embodiments, the transfer asset may be associated with a respective one or more of the types of data elements described above. In this way, the system may generate a data model that may build upon itself to comprise a plurality of layers as the system adds one or more new data assets, attributes, etc.

As may be further understood from FIG. 4, a particular data model may indicate one or more parties that have access to and/or use of the primary asset (e.g., storage asset). In such embodiments, the system may be configured to enable the one or more parties to access one or more pieces of data (e.g., personal data) stored by the storage asset.

As shown in FIG. 4, the data model may further comprise one or more collection assets (e.g., one or more data assets or individuals from which the storage asset receives data such as personal data). In the exemplary data model (e.g., visual data map) shown in this figure, the collection assets comprise a data subject (e.g., an individual that may provide data to the system for storage in the storage asset) and a collection asset (e.g., which may transfer one or more pieces of data that the collection asset has collected to the storage asset).

FIG. 5 depicts a portion of an exemplary data model that is populated for the primary data asset Gusto. Gusto is a software application that, in the example shown in FIG. 5, may serve as a human resources service that contains financial, expense, review, time and attendance, background, and salary information for one or more employees of a particular organization (e.g., GeneriTech). In the example of FIG. 5, the primary asset (e.g., Gusto) may be utilized by the HR (e.g., Human Resources) department of the particular organization (e.g., GeneriTech). Furthermore, the primary asset, Gusto, may collect financial information from one or more data subjects (e.g., employees of the particular organization), receive expense information transferred from Expensify (e.g., expensing software), and receive time and attendance data transferred from Kronos (e.g., timekeeping software). In the example shown in FIG. 5, access to the information collected and/or stored by Gusto may include, for example: (1) an ability to view and administer salary and background information by HR employees, and (2) an ability to view and administer employee review information by one or more service managers. In the example shown in this figure, personal and other data collected and stored by Gusto (e.g., salary information, etc.) may be transferred to a company banking system, to QuickBooks, and/or to an HR file cabinet.

As may be understood from the example shown in FIG. 5, the system may be configured to generate a data model based around Gusto that illustrates a flow of personal data utilized by Gusto. The data model in this example illustrates, for example, a source of personal data collected, stored and/or processed by Gusto, a destination of such data, an indication of who has access to such data within Gusto, and an organization and department responsible for the information collected by Gusto. In particular embodiments, the data model and accompanying visual representation (e.g., data map) generated by the system as described in any embodiment herein may be utilized in the context of compliance with one or more record keeping requirements related to the collection, storage, and processing of personal data.

FIGS. 6 and 7 depict an exemplary data model and related example that is similar, in some respects, to the data model and example of FIGS. 4 and 5. In the example shown in FIGS. 6 and 7, the exemplary data model and related example include a specific business process and processing activity that is associated with the primary asset (Gusto). In this example, the business process is compensation and the specific processing activity is direct deposit generation in Gusto. As may be understood from this figure, the collection and transfer of data related to the storage asset of Gusto is based on a need to generate direct deposits through Gusto in order to compensate employees. Gusto generates the information needed to conduct a direct deposit (e.g., financial and salary information) and then transmits this information to: (1) a company bank system for execution of the direct deposit; (2) Quickbooks for use in documenting the direct deposit payment; and (3) HR File cabinet for use in documenting the salary info and other financial information.

As may be understood in light of this disclosure, when generating such a data model, particular pieces of data (e.g., data attributes, data elements) may not be readily available to the system. In such embodiment, the system is configured to identify a particular type of data, create a placeholder for such data in memory, and seek out (e.g., scan for and populate) an appropriate piece of data to further populate the data model. For example, in particular embodiments, the system may identify Gusto as a primary asset and recognize that Gusto stores expense information. The system may then be configured to identify a source of the expense information (e.g., Expensify).

FIG. 8 depicts an exemplary screen display 800 that illustrates a visual representation (e.g., visual data map) of a data model (e.g., a data inventory). In the example shown in FIG. 8, the data map provides a visual indication of a flow of data collected from particular data subjects (e.g., employees 801). As may be understood from this figure, the data map illustrates that three separate data assets receive data (e.g., which may include personal data) directly from the employees 801. In this example, these three data assets include Kronos 803 (e.g., a human resources software application), Workday 805 (e.g., a human resources software application), and ADP 807 (e.g., a human resources software application and payment processor). As shown in FIG. 8, the transfer of data from the employees 801 to these assets is indicated by respective arrows.

As further illustrated in FIG. 8, the data map indicates a transfer of data from Workday 805 to ADP 807 as well as to a Recovery Datacenter 809 and a London HR File Center 811. As may be understood in light of this disclosure, the Recovery Datacenter 809 and London HR File Center 811 may comprise additional data assets in the context of the data model illustrated by the data map shown in FIG. 8. The Recover Datacenter 809 may include, for example, one or more computer servers (e.g., backup servers). The London HR File Center 811 may include, for example, one or more databases (e.g., such as the One or More Databases 140 shown in FIG. 1). AS shown in FIG. 8, each particular data asset depicted in the data map may be shown along with a visual indication of the type of data asset. For example, Kronos 803, Workday 805, and ADP 807 are depicted adjacent a first icon type (e.g., a computer monitor), while Recover Datacenter 809 and London HR File Center 811 are depicted adjacent a second and third icon type respectively (e.g., a server cluster and a file folder). In this way, the system may be configured to visually indicate, via the data model, particular information related to the data model in a relatively minimal manner.

FIG. 9 depicts an exemplary screen display 900 that illustrates a data map of a plurality of assets 905 in tabular form (e.g., table form). As may be understood from this figure, a table that includes one or more inventory attributes of each particular asset 905 in the table may indicate, for example: (1) a managing organization 910 of each respective asset 905; (2) a hosting location 915 of each respective asset 905 (e.g., a physical storage location of each asset 905); (3) a type 920 of each respective asset 905, if known (e.g., a database, software application, server, etc.); (4) a processing activity 925 associated with each respective asset 905; and/or (5) a status 930 of each particular data asset 905. In various embodiments, the status 930 of each particular asset 905 may indicate a status of the asset 905 in the discovery process. This may include, for example: (1) a “new” status for a particular asset that has recently been discovered as an asset that processes, stores, or collects personal data on behalf of an organization (e.g., discovered via one or more suitable techniques described herein); (2) an “in discovery” status for a particular asset for which the system is populating or seeking to populate one or more inventory attributes, etc.

FIG. 10 depicts an exemplary data map 1000 that includes an asset map of a plurality of data assets 1005A-F, which may, for example, be utilized by a particular entity in the collection, storage, and/or processing of personal data. As may be understood in light of this disclosure, the plurality of data assets 1005A-F may have been discovered using any suitable technique described herein (e.g., one or more intelligent identity scanning techniques, one or more questionnaires, one or more application programming interfaces, etc.). In various embodiments, a data inventory for each of the plurality of data assets 1005A-F may define, for each of the plurality of data assets 1005A-F a respective inventory attribute related to a storage location of the data asset.

As may be understood from this figure, the system may be configured to generate a map that indicates a location of the plurality of data assets 1005A-F for a particular entity. In the embodiment shown in this figure, locations that contain a data asset are indicated by circular indicia that contain the number of assets present at that location. In the embodiment shown in this figure, the locations are broken down by country. In particular embodiments, the asset map may distinguish between internal assets (e.g., first party servers, etc.) and external/third party assets (e.g., third party owned servers or software applications that the entity utilizes for data storage, transfer, etc.).

In some embodiments, the system is configured to indicate, via the visual representation, whether one or more assets have an unknown location (e.g., because the data model described above may be incomplete with regard to the location). In such embodiments, the system may be configured to: (1) identify the asset with the unknown location; (2) use one or more data modeling techniques described herein to determine the location (e.g., such as pinging the asset, generating one or more questionnaires for completion by a suitable individual, etc.); and (3) update a data model associated with the asset to include the location.

Data Model Population Module

In particular embodiments, a Data Model Population Module 1100 is configured to: (1) determine one or more unpopulated inventory attributes in a data model; (2) determine one or more attribute values for the one or more unpopulated inventory attributes; and (3) modify the data model to include the one or more attribute values.

Turning to FIG. 11, in particular embodiments, when executing the Data Model Population Module 1100, the system begins, at Step 1110, by analyzing one or more data inventories for each of the one or more data assets in the data model. The system may, for example, identify one or more particular data elements (e.g., inventory attributes) that make up the one or more data inventories. The system may, in various embodiments, scan one or more data structures associated with the data model to identify the one or more data inventories. In various embodiments, the system is configured to build an inventory of existing (e.g., known) data assets and identify inventory attributes for each of the known data assets.

Continuing to Step 1120, the system is configured to determine, for each of the one or more data inventories, one or more populated inventory attributes and one or more unpopulated inventory attributes (e.g., and/or one or more unpopulated data assets within the data model). As a particular example related to an unpopulated data asset, when generating and populating a data model, the system may determine that, for a particular asset, there is a destination asset. In various embodiments, the destination asset may be known (e.g., and already stored by the system as part of the data model). In any embodiment described herein, the destination asset may be unknown (e.g., a data element that comprises the destination asset may comprise a placeholder or other indication in memory for the system to populate the unpopulated inventory attribute (e.g., data element).

As another particular example, a particular storage asset may be associated with a plurality of inventory assets (e.g., stored in a data inventory associated with the storage asset). In this example, the plurality of inventory assets may include an unpopulated inventory attribute related to a type of personal data stored in the storage asset. The system may, for example, determine that the type of personal data is an unpopulated inventory asset for the particular storage asset.

Returning to Step 1130, the system is configured to determine, for each of the one or more unpopulated inventory attributes, one or more attribute values. In particular embodiments, the system may determine the one or more attribute values using any suitable technique (e.g., any suitable technique for populating the data model). In particular embodiments, the one or more techniques for populating the data model may include, for example: (1) obtaining data for the data model by using one or more questionnaires associated with a particular privacy campaign, processing activity, etc.; (2) using one or more intelligent identity scanning techniques discussed herein to identify personal data stored by the system and then map such data to a suitable data model; (3) using one or more application programming interfaces (API) to obtain data for the data model from another software application; and/or (4) using any other suitable technique. Exemplary techniques for determining the one or more attribute values are described more fully below. In any embodiment described herein, the system may be configured to use such techniques or other suitable techniques to populate one or more unpopulated data assets within the data model.

Next, at Step 1140, the system modifies the data model to include the one or more attribute values for each of the one or more unpopulated inventory attributes. The system may, for example, store the one or more attributes values in computer memory, associate the one or more attribute values with the one or more unpopulated inventory attributes, etc. In still any embodiment described herein, the system may modify the data model to include the one or more data assets identified as filling one or more vacancies left within the data model by the unpopulated one or more data assets.

Continuing to Step 1150, the system is configured to store the modified data model in memory. In various embodiments, the system is configured to store the modified data model in the One or More Databases 140, or in any other suitable location. In particular embodiments, the system is configured to store the data model for later use by the system in the processing of one or more data subject access requests. In any embodiment described herein, the system is configured to store the data model for use in one or more privacy impact assessments performed by the system.

Data Model Population Questionnaire Generation Module

In particular embodiments, a Data Population Questionnaire Generation Module 1200 is configured to generate a questionnaire (e.g., one or more questionnaires) comprising one or more questions associated with one or more particular unpopulated data attributes, and populate the unpopulated data attributes based at least in part on one or more responses to the questionnaire. In any embodiment described herein, the system may be configured to populate the unpopulated data attributes based on one or more responses to existing questionnaires.

In various embodiments, the one or more questionnaires may comprise one or more processing activity questionnaires (e.g., privacy impact assessments, data privacy impact assessments, etc.) configured to elicit one or more pieces of data related to one or more undertakings by an organization related to the collection, storage, and/or processing of personal data (e.g., processing activities). In particular embodiments, the system is configured to generate the questionnaire (e.g., a questionnaire template) based at least in part on one or more processing activity attributes, data asset attributes (e.g., inventory attributes), or other suitable attributes discussed herein.

Turning to FIG. 12, in particular embodiments, when executing the Data Population Questionnaire Generation Module 1200, the system begins, at Step 1210, by identifying one or more unpopulated data attributes from a data model. The system may, for example, identify the one or more unpopulated data attributes using any suitable technique described above. In particular embodiments, the one or more unpopulated data attributes may relate to, for example, one or more processing activity or asset attributes such as: (1) one or more processing activities associated with a particular data asset; (2) transfer data associated with the particular data asset (e.g., how and where the data stored and/or collected by the particular data asset is being transferred to and/or from); (3) personal data associated with the particular data assets asset (e.g., what type of personal data is collected and/or stored by the particular data asset; how, and from where, the data is collected, etc.); (4) storage data associated with the personal data (e.g., whether the data is being stored, protected and deleted); and (5) any other suitable attribute related to the collection, use, and transfer of personal data by one or more data assets or via one or more processing activities. In any embodiment described herein, the one or more unpopulated inventory attributes may comprise one or more other pieces of information such as, for example: (1) the type of data being stored by the particular data asset; (2) an amount of data stored by the particular data asset; (3) whether the data is encrypted by the particular data asset; (4) a location of the stored data (e.g., a physical location of one or more computer servers on which the data is stored by the particular data asset); etc.

Continuing to Step 1220, the system generates a questionnaire (e.g., a questionnaire template) comprising one or more questions associated with one or more particular unpopulated data attributes. As may be understood in light of the above, the one or more particulate unpopulated data attributes may relate to, for example, a particular processing activity or a particular data asset (e.g., a particular data asset utilized as part of a particular processing activity). In various embodiments, the one or more questionnaires comprise one or more questions associated with the unpopulated data attribute. For example, if the data model includes an unpopulated data attribute related to a location of a server on which a particular asset stores personal data, the system may generate a questionnaire associated with a processing activity that utilizes the asset (e.g., or a questionnaire associated with the asset). The system may generate the questionnaire to include one or more questions regarding the location of the server.

Returning to Step 1230, the system maps one or more responses to the one or more questions to the associated one or more particular unpopulated data attributes. The system may, for example, when generating the questionnaire, associate a particular question with a particular unpopulated data attribute in computer memory. In various embodiments, the questionnaire may comprise a plurality of question/answer pairings, where the answer in the question/answer pairings maps to a particular inventory attribute for a particular data asset or processing activity.

In this way, the system may, upon receiving a response to the particular question, substantially automatically populate the particular unpopulated data attribute. Accordingly, at Step 1240, the system modifies the data model to populate the one or more responses as one or more data elements for the one or more particular unpopulated data attributes. In particular embodiments, the system is configured to modify the data model such that the one or more responses are stored in association with the particular data element (e.g., unpopulated data attribute) to which the system mapped it at Step 1230. In various embodiments, the system is configured to store the modified data model in the One or More Databases 140, or in any other suitable location. In particular embodiments, the system is configured to store the data model for later use by the system in the processing of one or more data subject access requests. In any embodiment described herein, the system is configured to store the data model for use in one or more privacy impact assessments performed by the system.

Continuing to optional Step 1250, the system may be configured to modify the questionnaire based at least in part on the one or more responses. The system may, for example, substantially dynamically add and/or remove one or more questions to/from the questionnaire based at least in part on the one or more responses (e.g., one or more response received by a user completing the questionnaire). For example, the system may, in response to the user providing a particular inventory attribute or new asset, generates additional questions that relate to that particular inventory attribute or asset. The system may, as the system adds additional questions, substantially automatically map one or more responses to one or more other inventory attributes or assets. For example, in response to the user indicating that personal data for a particular asset is stored in a particular location, the system may substantially automatically generate one or more additional questions related to, for example, an encryption level of the storage, who has access to the storage location, etc.

In still any embodiment described herein, the system may modify the data model to include one or more additional assets, data attributes, inventory attributes, etc. in response to one or more questionnaire responses. For example, the system may modify a data inventory for a particular asset to include a storage encryption data element (which specifies whether the particular asset stores particular data in an encrypted format) in response to receiving such data from a questionnaire. Modification of a questionnaire is discussed more fully below with respect to FIG. 13.

Data Model Population Via Questionnaire Process Flow

FIG. 13 depicts an exemplary process flow 1300 for populating a data model (e.g., modifying a data model to include a newly discovered data asset, populating one or more inventory attributes for a particular processing activity or data asset, etc.). In particular, FIG. 13 depicts one or more exemplary data relationships between one or more particular data attributes (e.g., processing activity attributes and/or asset attributes), a questionnaire template (e.g., a processing activity template and/or a data asset template), a completed questionnaire (e.g., a processing activity assessment and/or a data asset assessment), and a data inventory (e.g., a processing activity inventory and/or an asset inventory). As may be understood from this figure the system is configured to: (1) identify new data assets; (2) generate an asset inventory for identified new data assets; and (3) populate the generated asset inventories. Systems and methods for populating the generated inventories are described more fully below.

As may be understood from FIG. 13, a system may be configured to map particular processing activity attributes 1320A to each of: (1) a processing activity template 1330A; and (2) a processing activity data inventory 1310A. As may be understood in light of this disclosure, the processing activity template 1330A may comprise a plurality of questions (e.g., as part of a questionnaire), which may, for example, be configured to elicit discovery of one or more new data assets. The plurality of questions may each correspond to one or more fields in the processing activity inventory 1310A, which may, for example, define one or more inventory attributes of the processing activity.

In particular embodiments, the system is configured to provide a processing activity assessment 1340A to one or more individuals for completion. As may be understood from FIG. 13, the system is configured to launch the processing activity assessment 1340A from the processing activity inventory 1310A and further configured to create the processing activity assessment 1340A from the processing activity template 1330. The processing activity assessment 1340A may comprise, for example, one or more questions related to the processing activity. The system may, in various embodiments, be configured to map one or more responses provided in the processing activity assessment 1340A to one or more corresponding fields in the processing activity inventory 1310A. The system may then be configured to modify the processing activity inventory 1310A to include the one or more responses and store the modified inventory in computer memory. In various embodiments, the system may be configured to approve a processing activity assessment 1340A (e.g., receive approval of the assessment) prior to feeding the processing activity inventory attribute values into one or more fields and/or cells of the inventory.

As may be further understood from FIG. 13, in response to creating a new asset record (e.g., which the system may create, for example, in response to a new asset discovery via the processing activity assessment 1340A described immediately above, or in any other suitable manner), the system may generate an asset inventory 1310B (e.g., a data asset inventory) that defines a plurality of inventory attributes for the new asset (e.g., new data asset).

As may be understood from FIG. 13, a system may be configured to map particular asset attributes 1320B to each of: (1) an asset template 1330BA; and (2) an asset inventory 1310A. As may be understood in light of this disclosure, the asset template 1330B may comprise a plurality of questions (e.g., as part of a questionnaire), which may, for example, be configured to elicit discovery of one or more processing activities associated with the asset and/or one or more inventory attributes of the asset. The plurality of questions may each correspond to one or more fields in the asset inventory 1310B, which may, for example, define one or more inventory attributes of the asset.

In particular embodiments, the system is configured to provide an asset assessment 1340B to one or more individuals for completion. As may be understood from FIG. 13, the system is configured to launch the asset assessment 1340B from the asset inventory 1310B and further configured to create the asset assessment 1340B from the asset template 1330B. The asset assessment 1340B may comprise, for example, one or more questions related to the data asset. The system may, in various embodiments, be configured to map one or more responses provided in the asset assessment 1340B to one or more corresponding fields in the asset inventory 1310B. The system may then be configured to modify the asset inventory 1310B (e.g., and/or a related processing activity inventory 1310A) to include the one or more responses and store the modified inventory in computer memory. In various embodiments, the system may be configured to approve an asset assessment 1340B (e.g., receive approval of the assessment) prior to feeding the asset inventory attribute values into one or more fields and/or cells of the inventory.

FIG. 13 further includes a detail view 1350 of a relationship between particular data attributes 1320C with an exemplary data inventory 1310C and a questionnaire template 1330C. As may be understood from this detail view 1350, a particular attribute name may map to a particular question title in a template 1330C as well as to a field name in an exemplary data inventory 1310C. In this way, the system may be configured to populate (e.g., automatically populate) a field name for a particular inventory 1310C in response to a user providing a question title as part of a questionnaire template 1330C. Similarly, a particular attribute description may map to a particular question description in a template 1330C as well as to a tooltip on a fieldname in an exemplary data inventory 1310C. In this way, the system may be configured to provide the tooltip for a particular inventory 1310C that includes the question description provided by a user as part of a questionnaire template 1330C.

As may be further understood from the detail view 1350 of FIG. 13, a particular response type may map to a particular question type in a template 1330C as well as to a field type in an exemplary data inventory 1310C. A particular question type may include, for example, a multiple-choice question (e.g., A, B, C, etc.), a freeform response, an integer value, a drop-down selection, etc. A particular field type may include, for example, a memo field type, a numeric field type, an integer field type, a logical field type, or any other suitable field type. A particular data attribute may require a response type of, for example: (1) a name of an organization responsible for a data asset (e.g., a free form response); (2) a number of days that data is stored by the data asset (e.g., an integer value); and/or (3) any other suitable response type.

In still any embodiment described herein, the system may be configured to map a one or more attribute values to one or more answer choices in a template 1330C as well as to one or more lists and/or responses in a data inventory 1310C. The system may then be configured to populate a field in the data inventory 1310C with the one or more answer choices provided in a response to a question template 1330C with one or more attribute values.

Exemplary Questionnaire Generation and Completion User Experience

FIGS. 14-25 depict exemplary screen displays that a user may encounter when generating a questionnaire (e.g., one or more questionnaires and/or templates) for populating one or more data elements (e.g., inventory attributes) of a data model for a data asset and/or processing activity. FIG. 14, for example, depicts an exemplary asset-based questionnaire template builder 1400. As may be understood from FIG. 14, the template builder may enable a user to generate an asset-based questionnaire template that includes one or more sections 1420 related to the asset (e.g., asset information, security, disposal, processing activities, etc.). As may be understood in light of this disclosure, the system may be configured to substantially automatically generate an asset-based questionnaire template based at least in part on the one or more unpopulated inventory attributes discussed above. The system may, for example, be configured to generate a template that is configured to populate the one or more unpopulated attributes (e.g., by eliciting responses, via a questionnaire to one or more questions that are mapped to the attributes within the data inventory).

In various embodiments, the system is configured to enable a user to modify a default template (e.g., or a system-created template) by, for example, adding additional sections, adding one or more additional questions to a particular section, etc. In various embodiments, the system may provide one or more tools for modifying the template. For example, in the embodiment shown in FIG. 14, the system may provide a user with a draft and drop question template 1410, from which the user may select a question type (e.g., textbox, multiple choice, etc.).

A template for an asset may include, for example: (1) one or more questions requesting general information about the asset; (2) one or more security-related questions about the asset; (3) one or more questions regarding how the data asset disposes of data that it uses; and/or (4) one or more questions regarding processing activities that involve the data asset. In various embodiments, each of these one or more sections may comprise one or more specific questions that may map to particular portions of a data model (e.g., a data map).

FIG. 15 depicts an exemplary screen display of a processing activity questionnaire template builder 1500. The screen display shown in FIG. 15 is similar to the template builder shown in FIG. 14 with respect to the data asset-based template builder. As may be understood from FIG. 15, the template builder may enable a user to generate a processing activity-based questionnaire template that includes one or more sections 1420 related to the processing activity (e.g., business process information, personal data, source, storage, destinations, access and use, etc.). As may be understood in light of this disclosure, the system may be configured to substantially automatically generate a processing activity-based questionnaire template based at least in part on the one or more unpopulated inventory attributes related to the processing activity (e.g., as discussed above). The system may, for example, be configured to generate a template that is configured to populate the one or more unpopulated attributes (e.g., by eliciting responses, via a questionnaire to one or more questions that are mapped to the attributes within the data inventory).

In various embodiments, the system is configured to enable a user to modify a default template (e.g., or a system-created template) by, for example, adding additional sections, adding one or more additional questions to a particular section, etc. In various embodiments, the system may provide one or more tools for modifying the template. For example, in the embodiment shown in FIG. 15, the system may provide a user with a draft and drop question template 1510, from which the user may select a question type (e.g., textbox, multiple choice, asset attributes, data subjects, etc.). The system may be further configured to enable a user to publish a completed template (e.g., for use in a particular assessment). In any embodiment described herein, the system may be configured to substantially automatically publish the template.

In various embodiments, a template for a processing activity may include, for example: (1) one or more questions related to the type of business process that involves a particular data asset; (2) one or more questions regarding what type of personal data is acquired from data subjects for use by a particular data asset; (3) one or more questions related to a source of the acquired personal data; (4) one or more questions related to how and/or where the personal data will be stored and/or for how long; (5) one or more questions related to one or more other data assets that the personal data will be transferred to; and/or (6) one or more questions related to who will have the ability to access and/or use the personal data.

Continuing to FIG. 16, an exemplary screen display 1600 depicts a listing of assets 1610 for a particular entity. These may, for example, have been identified as part of the data model generation system described above. As may be understood from this figure, a user may select a drop-down indicator 1615 to view more information about a particular asset. In the exemplary embodiment shown in FIG. 16, the system stores the managing organization group for the “New Asset”, but is missing some additional information (e.g., such as a description 1625 of the asset). In order to fill out the missing inventory attributes for the “New Asset”, the system, in particular embodiments, is configured to enable a user to select a Send Assessment indicia 1620 in order to transmit an assessment related to the selected asset to an individual tasked with providing one or more pieces of information related to the asset (e.g., a manager, or other individual with knowledge of the one or more inventory attributes).

In response to the user selecting the Send Assessment indicia 1620, the system may create the assessment based at least in part on a template associated with the asset and transmit the assessment to a suitable individual for completion (e.g., and/or transmit a request to the individual to complete the assessment).

FIG. 17 depicts an exemplary assessment transmission interface 1700 via which a user can transmit one or more assessments for completion. As shown in this figure, the user may assign a respondent, provide a deadline, indicate a reminder time, and provide one or more comments using an assessment request interface 1710. The user may then select a Send Assessment(s) indicia 1720 in order to transmit the assessment.

FIG. 18 depicts an exemplary assessment 1800 which a user may encounter in response to receiving a request to complete the assessment as described above with respect to FIGS. 16 and 17. As shown in FIG. 18, the assessment 1800 may include one or more questions that map to the one or more unpopulated attributes for the asset shown in FIG. 16. For example, the one or more questions may include a question related to a description of the asset, which may include a free form text box 1820 for providing a description of the asset. FIG. 19 depicts an exemplary screen display 1900 with the text box 1920 completed, where the description includes a value of “Value_1”. As shown in FIGS. 18 and 19, the user may have renamed “New Asset” (e.g., which may have included a default or placeholder name) shown in FIGS. 16 and 17 to “7^(th) Asset.”

Continuing to FIG. 20, the exemplary screen display 2000 depicts the listing of assets 2010 from FIG. 16 with some additional attributes populated. For example, the Description 2025 (e.g., “Value_1”) provided in FIG. 19 has been added to the inventory. As may be understood in light of this disclosure, in response to a user providing the description via the assessment shown in FIGS. 18 and 19, the system may be configured to map the provided description to the attribute value associated with the description of the asset in the data inventory. The system may have then modified the data inventory for the asset to include the description attribute. In various embodiments, the system is configured to store the modified data inventory as part of a data model (e.g., in computer memory).

FIGS. 21-24 depict exemplary screen displays showing exemplary questions that make up part of a processing activity questionnaire (e.g., assessment). FIG. 21 depicts an exemplary interface 2100 for responding to a first question 2110 and a second question 2120. As shown in FIG. 21, the first question 2110 relates to whether the processing activity is a new or existing processing activity. The first question 2110 shown in FIG. 21 is a multiple-choice question. The second question 2120 relates to whether the organization is conducting the activity on behalf of another organization. As shown in this figure, the second question 2120 includes both a multiple-choice portion and a free-form response portion.

As discussed above, in various embodiments, the system may be configured to modify a questionnaire in response to (e.g., based on) one or more responses provided by a user completing the questionnaire. In particular embodiments, the system is configured to modify the questionnaire substantially on-the-fly (e.g., as the user provides each particular answer). FIG. 22 depicts an interface 2200 that includes a second question 2220 that differs from the second question 2120 shown in FIG. 21. As may be understood in light of this disclosure, in response to the user providing a response to the first question 2110 in FIG. 21 that indicates that the processing activity is a new processing activity, the system may substantially automatically modify the second question 2120 from FIG. 21 to the second question 2220 from FIG. 22 (e.g., such that the second question 2220 includes one or more follow up questions or requests for additional information based on the response to the first question 2110 in FIG. 21).

As shown in FIG. 22, the second question 2220 requests a description of the activity that is being pursued. In various embodiments (e.g., such as if the user had selected that the processing activity was an existing one), the system may not modify the questionnaire to include the second question 2220 from FIG. 22, because the system may already store information related to a description of the processing activity at issue. In various embodiments, any suitable question described herein may include a tooltip 2225 on a field name (e.g., which may provide one or more additional pieces of information to guide a user's response to the questionnaire and/or assessment).

FIGS. 23 and 24 depict additional exemplary assessment questions. The questions shown in these figures relate to, for example, particular data elements processed by various aspects of a processing activity.

FIG. 25 depicts a dashboard 2500 that includes an accounting of one or more assessments that have been completed, are in progress, or require completion by a particular organization. The dashboard 2500 shown in this figure is configured to provide information relate to the status of one or more outstanding assessments. As may be understood in light of this disclosure, because of the volume of assessment requests, it may be necessary to utilize one or more third party organizations to facilitate a timely completion of one or more assessment requests. In various embodiments, the dashboard may indicate that, based on a fact that a number of assessments are still in progress or incomplete, that a particular data model for an entity, data asset, processing activity, etc. remains incomplete. In such embodiments, an incomplete nature of a data model may raise one or more flags or indicate a risk that an entity may not be in compliance with one or more legal or industry requirements related to the collection, storage, and/or processing of personal data.

Intelligent Identity Scanning Module

Turning to FIG. 26, in particular embodiments, the Intelligent Identity Scanning Module 2600 is configured to scan one or more data sources to identify personal data stored on one or more network devices for a particular organization, analyze the identified personal data, and classify the personal data (e.g., in a data model) based at least in part on a confidence score derived using one or more machine learning techniques. The confidence score may be and/or comprise, for example, an indication of the probability that the personal data is actually associated with a particular data subject (e.g., that there is at least an 80% confidence level that a particular phone number is associated with a particular individual.)

When executing the Intelligent Identity Scanning Module 2600, the system begins, at Step 2610, by connecting to one or more databases or other data structures, and scanning the one or more databases to generate a catalog of one or more individuals and one or more pieces of personal information associated with the one or more individuals. The system may, for example, be configured to connect to one or more databases associated with a particular organization (e.g., one or more databases that may serve as a storage location for any personal or other data collected, processed, etc. by the particular organization, for example, as part of a suitable processing activity. As may be understood in light of this disclosure, a particular organization may use a plurality of one or more databases (e.g., the One or More Databases 140 shown in FIG. 1), a plurality of servers (e.g., the One or More Third Party Servers 160 shown in FIG. 1), or any other suitable data storage location in order to store personal data and other data collected as part of any suitable privacy campaign, privacy impact assessment, processing activity, etc.

In particular embodiments, the system is configured to scan the one or more databases by searching for particular data fields comprising one or more pieces of information that may include personal data. The system may, for example, be configured to scan and identify one of more pieces of personal data such as: (1) name; (2) address; (3) telephone number; (4) e-mail address; (5) social security number; (6) information associated with one or more credit accounts (e.g., credit card numbers); (7) banking information; (8) location data; (9) internet search history; (10) non-credit account data; and/or (11) any other suitable personal information discussed herein. In particular embodiments, the system is configured to scan for a particular type of personal data (e.g., or one or more particular types of personal data).

The system may, in various embodiments, be further configured to generate a catalog of one or more individuals that also includes one or more pieces of personal information (e.g., personal data) identified for the individuals during the scan. The system may, for example, in response to discovering one or more pieces of personal data in a particular storage location, identify one or more associations between the discovered pieces of personal data. For example, a particular database may store a plurality of individuals' names in association with their respective telephone numbers. One or more other databases may include any other suitable information.

The system may, for example, generate the catalog to include any information associated with the one or more individuals identified in the scan. The system may, for example, maintain the catalog in any suitable format (e.g., a data table, etc.).

Continuing to Step 2620, the system is configured to scan one or more structured and/or unstructured data repositories based at least in part on the generated catalog to identify one or more attributes of data associated with the one or more individuals. The system may, for example, be configured to utilize information discovered during the initial scan at Step 2610 to identify the one or more attributes of data associated with the one or more individuals.

For example, the catalog generated at Step 2610 may include a name, address, and phone number for a particular individual. The system may be configured, at Step 2620, to scan the one or more structured and/or unstructured data repositories to identify one or more attributes that are associated with one or more of the particular individual's name, address and/or phone number. For example, a particular data repository may store banking information (e.g., a bank account number and routing number for the bank) in association with the particular individual's address. In various embodiments, the system may be configured to identify the banking information as an attribute of data associated with the particular individual. In this way, the system may be configured to identify particular data attributes (e.g., one or more pieces of personal data) stored for a particular individual by identifying the particular data attributes using information other than the individual's name.

Returning to Step 2630, the system is configured to analyze and correlate the one or more attributes and metadata for the scanned one or more structured and/or unstructured data repositories. In particular embodiments, the system is configured to correlate the one or more attributes with metadata for the associated data repositories from which the system identified the one or more attributes. In this way, the system may be configured to store data regarding particular data repositories that store particular data attributes.

In particular embodiments, the system may be configured to cross-reference the data repositories that are discovered to store one or more attributes of personal data associated with the one or more individuals with a database of known data assets. In particular embodiments, the system is configured to analyze the data repositories to determine whether each data repository is part of an existing data model of data assets that collect, store, and/or process personal data. In response to determining that a particular data repository is not associated with an existing data model, the system may be configured to identify the data repository as a new data asset (e.g., via asset discovery), and take one or more actions (e.g., such as any suitable actions described herein) to generate and populate a data model of the newly discovered data asset. This may include, for example: (1) generating a data inventory for the new data asset; (2) populating the data inventory with any known attributes associated with the new data asset; (3) identifying one or more unpopulated (e.g., unknown) attributes of the data asset; and (4) taking any suitable action described herein to populate the unpopulated data attributes.

In particular embodiments, the system my, for example: (1) identify a source of the personal data stored in the data repository that led to the new asset discovery; (2) identify one or more relationships between the newly discovered asset and one or more known assets; and/or (3) etc.

Continuing to Step 2640, the system is configured to use one or more machine learning techniques to categorize one or more data elements from the generated catalog, analyze a flow of the data among the one or more data repositories, and/or classify the one or more data elements based on a confidence score as discussed below.

Continuing to Step 2650, the system, in various embodiments, is configured to receive input from a user confirming or denying a categorization of the one or more data elements, and, in response, modify the confidence score. In various embodiments, the system is configured to iteratively repeat Steps 2640 and 2650. In this way, the system is configured to modify the confidence score in response to a user confirming or denying the accuracy of a categorization of the one or more data elements. For example, in particular embodiments, the system is configured to prompt a user (e.g., a system administrator, privacy officer, etc.) to confirm that a particular data element is, in fact, associated with a particular individual from the catalog. The system may, in various embodiments, be configured to prompt a user to confirm that a data element or attribute discovered during one or more of the scans above were properly categorized at Step 2640.

In particular embodiments, the system is configured to modify the confidence score based at least in part on receiving one or more confirmations that one or more particular data elements or attributes discovered in a particular location during a scan are associated with particular individuals from the catalog. As may be understood in light of this disclosure, the system may be configured to increase the confidence score in response to receiving confirmation that particular types of data elements or attributes discovered in a particular storage location are typically confirmed as being associated with particular individuals based on one or more attributes for which the system was scanning.

Exemplary Intelligent Identity Scanning Technical Platforms

FIG. 27 depicts an exemplary technical platform via which the system may perform one or more of the steps described above with respect to the Intelligent Identity Scanning Module 2600. As shown in the embodiment in this figure, an Intelligent Identity Scanning System 2600 comprises an Intelligent Identity Scanning Server 130, such as the Intelligent Identity Scanning Server 130 described above with respect to FIG. 1. The Intelligent Identity Scanning Server 130 may, for example, comprise a processing engine (e.g., one or more computer processors). In some embodiments, the Intelligent Identity Scanning Server 130 may include any suitable cloud hosted processing engine (e.g., one or more cloud-based computer servers). In particular embodiments, the Intelligent Identity Scanning Server 130 is hosted in a Microsoft Azure cloud.

In particular embodiments, the Intelligent Identity Scanning Server 130 is configured to sit outside one or more firewalls (e.g., such as the firewall 195 shown in FIG. 26). In such embodiments, the Intelligent Identity Scanning Server 130 is configured to access One or More Remote Computing Devices 150 through the Firewall 195 (e.g., one or more firewalls) via One or More Networks 115 (e.g., such as any of the One or More Networks 115 described above with respect to FIG. 1).

In particular embodiments, the One or More Remote Computing Devices 150 include one or more computing devices that make up at least a portion of one or more computer networks associated with a particular organization. In particular embodiments, the one or more computer networks associated with the particular organization comprise one or more suitable servers, one or more suitable databases, one or more privileged networks, and/or any other suitable device and/or network segment that may store and/or provide for the storage of personal data. In the embodiment shown in FIG. 27, the one or more computer networks associated with the particular organization may comprise One or More Third Party Servers 160, One or More Databases 140, etc. In particular embodiments, the One or More Remote Computing Devices 150 are configured to access one or more segments of the one or more computer networks associated with the particular organization. In some embodiments, the one or more computer networks associated with the particular organization comprise One or More Privileged Networks 165. In still any embodiment described herein, the one or more computer networks comprise one or more network segments connected via one or more suitable routers, one or more suitable network hubs, one or more suitable network switches, etc.

As shown in FIG. 27, various components that make up one or more parts of the one or more computer networks associated with the particular organization may store personal data (e.g., such as personal data stored on the One or More Third Party Servers 160, the One or More Databases 140, etc.). In various embodiments, the system is configured to perform one or more steps related to the Intelligent Identity Scanning Server 2600 in order to identify the personal data for the purpose of generating the catalog of individuals described above (e.g., and/or identify one or more data assets within the organization's network that store personal data)

As further shown in FIG. 27, in various embodiments, the One or More Remote Computing Devices 150 may store a software application (e.g., the Intelligent Identity Scanning Module). In such embodiments, the system may be configured to provide the software application for installation on the One or More Remote Computing Devices 150. In particular embodiments, the software application may comprise one or more virtual machines. In particular embodiments, the one or more virtual machines may be configured to perform one or more of the steps described above with respect to the Intelligent Identity Scanning Module 2600 (e.g., perform the one or more steps locally on the One or More Remote Computing Devices 150).

In various embodiments, the one or more virtual machines may have the following specifications: (1) any suitable number of cores (e.g., 4, 6, 8, etc.); (2) any suitable amount of memory (e.g., 4 GB, 8 GB, 16 GB etc.); (3) any suitable operating system (e.g., CentOS 7.2); and/or (4) any other suitable specification. In particular embodiments, the one or more virtual machines may, for example, be used for one or more suitable purposes related to the Intelligent Identity Scanning System 2700. These one or more suitable purposes may include, for example, running any of the one or more modules described herein, storing hashed and/or non-hashed information (e.g., personal data, personally identifiable data, catalog of individuals, etc.), storing and running one or more searching and/or scanning engines (e.g., Elasticsearch), etc.

In various embodiments, the Intelligent Identity Scanning System 2700 may be configured to distribute one or more processes that make up part of the Intelligent Identity Scanning Process (e.g., described above with respect to the Intelligent Identity Scanning Module 1800). The one or more software applications installed on the One or more Remote Computing Devices 150 may, for example, be configured to provide access to the one or more computer networks associated with the particular organization to the Intelligent Identity Scanning Server 130. The system may then be configured to receive, from the One or more Remote Computing Devices 150 at the Intelligent Identity Scanning Server 130, via the Firewall 195 and One or More Networks 115, scanned data for analysis.

In particular embodiments, the Intelligent Identity Scanning System 2700 is configured to reduce an impact on a performance of the One or More Remote Computing Devices 150, One or More Third Party Servers 160 and other components that make up one or more segments of the one or more computer networks associated with the particular organization. For example, in particular embodiments, the Intelligent Identity Scanning System 2700 may be configured to utilize one or more suitable bandwidth throttling techniques. In any embodiment described herein, the Intelligent Identity Scanning System 2700 is configured to limit scanning (e.g., any of the one or more scanning steps described above with respect to the Intelligent Identity Scanning Module 2600) and other processing steps (e.g., one or more steps that utilize one or more processing resources) to non-peak times (e.g., during the evening, overnight, on weekends and/or holidays, etc.). In any embodiment described herein, the system is configured to limit performance of such processing steps to backup applications and data storage locations. The system may, for example, use one or more sampling techniques to decrease a number of records required to scan during the personal data discovery process.

FIG. 28 depicts an exemplary asset access methodology that the system may utilize in order to access one or more network devices that may store personal data (e.g., or other personally identifiable information). As may be understood from this figure, the system may be configured to access the one or more network devices using a locally deployed software application (e.g., such as the software application described immediately above). In various embodiments, the software application is configured to route identity scanning traffic through one or more gateways, configure one or more ports to accept one or more identity scanning connections, etc.

As may be understood from this figure, the system may be configured to utilize one or more credential management techniques to access one or more privileged network portions. The system may, in response to identifying particular assets or personally identifiable information via a scan, be configured to retrieve schema details such as, for example, an asset ID, Schema ID, connection string, credential reference URL, etc. In this way, the system may be configured to identify and store a location of any discovered assets or personal data during a scan.

Data Subject Access Request Fulfillment Module

Turning to FIG. 29, in particular embodiments, a Data Subject Access Request Fulfillment Module 2900 is configured to receive a data subject access request, process the request, and fulfill the request based at least in part on one or more request parameters. In various embodiments, an organization, corporation, etc. may be required to provide information requested by an individual for whom the organization stores personal data within a certain time period (e.g., 30 days). As a particular example, an organization may be required to provide an individual with a listing of, for example: (1) any personal data that the organization is processing for an individual, (2) an explanation of the categories of data being processed and the purpose of such processing; and/or (3) categories of third parties to whom the data may be disclosed.

Various privacy and security policies (e.g., such as the European Union's General Data Protection Regulation, and other such policies) may provide data subjects (e.g., individuals, organizations, or other entities) with certain rights related to the data subject's personal data that is collected, stored, or otherwise processed by an organization. These rights may include, for example: (1) a right to obtain confirmation of whether a particular organization is processing their personal data; (2) a right to obtain information about the purpose of the processing (e.g., one or more reasons for which the personal data was collected); (3) a right to obtain information about one or more categories of data being processed (e.g., what type of personal data is being collected, stored, etc.); (4) a right to obtain information about one or more categories of recipients with whom their personal data may be shared (e.g., both internally within the organization or externally); (5) a right to obtain information about a time period for which their personal data will be stored (e.g., or one or more criteria used to determine that time period); (6) a right to obtain a copy of any personal data being processed (e.g., a right to receive a copy of their personal data in a commonly used, machine-readable format); (7) a right to request erasure (e.g., the right to be forgotten), rectification (e.g., correction or deletion of inaccurate data), or restriction of processing of their personal data; and (8) any other suitable rights related to the collection, storage, and/or processing of their personal data (e.g., which may be provided by law, policy, industry or organizational practice, etc.).

As may be understood in light of this disclosure, a particular organization may undertake a plurality of different privacy campaigns, processing activities, etc. that involve the collection and storage of personal data. In some embodiments, each of the plurality of different processing activities may collect redundant data (e.g., may collect the same personal data for a particular individual more than once), and may store data and/or redundant data in one or more particular locations (e.g., on one or more different servers, in one or more different databases, etc.). In this way, a particular organization may store personal data in a plurality of different locations which may include one or more known and/or unknown locations. As such, complying with particular privacy and security policies related to personal data (e.g., such as responding to one or more requests by data subjects related to their personal data) may be particularly difficult (e.g., in terms of cost, time, etc.). In particular embodiments, a data subject access request fulfillment system may utilize one or more data model generation and population techniques (e.g., such as any suitable technique described herein) to create a centralized data map with which the system can identify personal data stored, collected, or processed for a particular data subject, a reason for the processing, and any other information related to the processing.

Turning to FIG. 29, when executing the Data Subject Access Request Fulfillment Module 2900, the system begins, at Step 2910, by receiving a data subject access request. In various embodiments, the system receives the request via a suitable web form. In certain embodiments, the request comprises a particular request to perform one or more actions with any personal data stored by a particular organization regarding the requestor. For example, in some embodiments, the request may include a request to view one or more pieces of personal data stored by the system regarding the requestor. In any embodiment described herein, the request may include a request to delete one or more pieces of personal data stored by the system regarding the requestor. In still any embodiment described herein, the request may include a request to update one or more pieces of personal data stored by the system regarding the requestor. In still any embodiment described herein, the request may include a request based on any suitable right afforded to a data subject, such as those discussed above.

Continuing to Step 2920, the system is configured to process the request by identifying and retrieving one or more pieces of personal data associated with the requestor that are being processed by the system. For example, in various embodiments, the system is configured to identify any personal data stored in any database, server, or other data repository associated with a particular organization. In various embodiments, the system is configured to use one or more data models, such as those described above, to identify this personal data and suitable related information (e.g., where the personal data is stored, who has access to the personal data, etc.). In various embodiments, the system is configured to use intelligent identity scanning (e.g., as described above) to identify the requestor's personal data and related information that is to be used to fulfill the request.

In still any embodiment described herein, the system is configured to use one or more machine learning techniques to identify such personal data. For example, the system may identify particular stored personal data based on, for example, a country in which a website that the data subject request was submitted is based, or any other suitable information.

In particular embodiments, the system is configured to scan and/or search one or more existing data models (e.g., one or more current data models) in response to receiving the request in order to identify the one or more pieces of personal data associated with the requestor. The system may, for example, identify, based on one or more data inventories (e.g., one or more inventory attributes) a plurality of storage locations that store personal data associated with the requestor. In any embodiment described herein, the system may be configured to generate a data model or perform one or more scanning techniques in response to receiving the request (e.g., in order to automatically fulfill the request).

Returning to Step 2930, the system is configured to take one or more actions based at least in part on the request. In some embodiments, the system is configured to take one or more actions for which the request was submitted (e.g., display the personal data, delete the personal data, correct the personal data, etc.). In particular embodiments, the system is configured to take the one or more actions substantially automatically. In particular embodiments, in response a data subject submitting a request to delete their personal data from an organization's systems, the system may: (1) automatically determine where the data subject's personal data is stored; and (2) in response to determining the location of the data (which may be on multiple computing systems), automatically facilitate the deletion of the data subject's personal data from the various systems (e.g., by automatically assigning a plurality of tasks to delete data across multiple business systems to effectively delete the data subject's personal data from the systems). In particular embodiments, the step of facilitating the deletion may comprise, for example: (1) overwriting the data in memory; (2) marking the data for overwrite; (2) marking the data as free (e.g., and deleting a directory entry associated with the data); and/or (3) any other suitable technique for deleting the personal data. In particular embodiments, as part of this process, the system uses an appropriate data model (see discussion above) to efficiently determine where all of the data subject's personal data is stored.

Data Subject Access Request User Experience

FIGS. 30-31 depict exemplary screen displays that a user may view when submitting a data subject access request. As shown in FIG. 30, a website 3000 associated with a particular organization may include a user-selectable indicium 3005 for submitting a privacy-related request. A user desiring to make such a request may select the indicia 3005 in order to initiate the data subject access request process.

FIG. 31 depicts an exemplary data subject access request form in both an unfilled and filled out state. As shown in this figure, the system may prompt a user to provide information such as, for example: (1) what type of requestor the user is (e.g., employee, customer, etc.); (2) what the request involves (e.g., requesting info, opting out, deleting data, updating data, etc.); (3) first name; (4) last name; (5) email address; (6) telephone number; (7) home address; and/or (8) one or more details associated with the request.

As discussed in more detail above, a data subject may submit a subject access request, for example, to request a listing of any personal information that a particular organization is currently storing regarding the data subject, to request that the personal data be deleted, to opt out of allowing the organization to process the personal data, etc.

Alternative Embodiment

In particular embodiments, a data modeling or other system described herein may include one or more features in addition to those described. Various such alternative embodiments are described below.

Processing Activity and Data Asset Assessment Risk Flagging

In particular embodiments, the questionnaire template generation system and assessment system described herein may incorporate one or more risk flagging systems. FIGS. 32-35 depict exemplary user interfaces that include risk flagging of particular questions within a processing activity assessment. As may be understood from these figures, a user may select a flag risk indicium to provide input related to a description of risks and mitigation of a risk posed by one or more inventory attributes associated with the question. As shown in these figures, the system may be configured to substantially automatically assign a risk to a particular response to a question in a questionnaire. In various embodiments, the assigned risk is determined based at least in part on the template from which the assessment was generated.

In particular embodiments, the system may utilize the risk level assigned to particular questionnaire responses as part of a risk analysis of a particular processing activity or data asset. Various techniques for assessing the risk of various privacy campaigns are described in U.S. patent application Ser. No. 15/256,419, filed Sep. 2, 2016, entitled “Data processing systems and methods for operationalizing privacy compliance and assessing the risk of various respective privacy campaigns,” which is hereby incorporated herein in its entirety.

Centralized Repository of Personally Identifiable Information (PII) Overview

A centralized data repository system, in various embodiments, is configured to provide a central data-storage repository (e.g., one or more servers, databases, etc.) for the centralized storage of personally identifiable information (PII) and/or personal data for one or more particular data subjects. In particular embodiments, the centralized data repository may enable the system to populate one or more data models (e.g., using one or more suitable techniques described above) substantially on-the-fly (e.g., as the system collects, processes, stores, etc. personal data regarding a particular data subject). In this way, in particular embodiments, the system is configured to maintain a substantially up-to-date data model for a plurality of data subjects (e.g., each particular data subject for whom the system collects, processes, stores, etc. personal data). The system may then be configured to substantially automatically respond to one or more data access requests by a data subject (e.g., individual, entity, organization, etc.), for example, using the substantially up-to-date data model. In particular embodiments, the system may be configured to respond to the one or more data access requests using any suitable technique described herein.

As may be understood in light of this disclosure, a particular organization may undertake a plurality of different privacy campaigns, processing activities, etc. that involve the collection and storage of personal data. In some embodiments, each of the plurality of different processing activities may collect redundant data (e.g., may collect the same personal data for a particular individual more than once), and may store data and/or redundant data in a plurality of different locations (e.g., on one or more different servers, in one or more different databases, etc.). In this way, a particular organization may store personal data in a plurality of different locations which may include one or more known and/or unknown locations. As such, complying with particular privacy and security policies related to personal data (e.g., such as responding to one or more requests by data subjects related to their personal data) may be particularly difficult (e.g., in terms of cost, time, etc.). Accordingly, utilizing and maintaining a centralized data repository for PII may enable the system to more quickly and accurately respond to data subject access requests and other requests related to collected, stored, and processed personal data. In particular embodiments, the centralized data repository may include one or more third party data repositories (e.g., one or more third party data repositories maintained on behalf of a particular entity that collects, stores, and/or processes personal data).

In various embodiments, a third-party data repository system is configured to facilitate the receipt and centralized storage of personal data for each of a plurality of respective data subjects. In particular embodiments, the system may be configured to: (1) receive personal data associated with a particular data subject (e.g., a copy of the data, a link to a location of where the data is stored, etc.); and (2) store the personal data in a suitable data format (e.g., a data model, a reference table, etc.) for later retrieval. In any embodiment described herein, the system may be configured to receive an indication that personal data has been collected regarding a particular data subject (e.g., collected by a first party system, a software application utilized by a particular entity, etc.).

In particular embodiments, the third party data repository system is configured to: (1) receive an indication that a first party system (e.g., entity) has collected and/or processed a piece of personal data for a data subject; (2) determine a location in which the first party system has stored the piece of personal data; (3) optionally digitally store (e.g., in computer memory) a copy of the piece of personal data and associate, in memory, the piece of personal data with the data subject; and (4) optionally digitally store an indication of the storage location utilized by the first party system for the piece of personal data. In particular embodiments, the system is configured to provide a centralized database, for each particular data subject (e.g., each particular data subject about whom a first party system collects or has collected personally identifiable information), of any personal data processed and/or collected by a particular entity.

In particular embodiments, a third-party data repository system is configured to interface with a consent receipt management system (e.g., such as the consent receipt management system described below). In particular embodiments, the system may, for example: (1) receive an indication of a consent receipt having an associated unique subject identifier and one or more receipt definitions (e.g., such as any suitable definition described herein); (2) identify, based at least in part on the one or more receipt definitions, one or more pieces of repository data associated with the consent receipt (e.g., one or more data elements or pieces of personal data for which the consent receipt provides consent to process; a storage location of the one or more data elements for which the consent receipt provides consent to process; etc.); (3) digitally store the unique subject identifier in one or more suitable data stores; and (4) digitally associate the unique subject identifier with the one or more pieces of repository data. In particular embodiments, the system is configured to store the personal data provided as part of the consent receipt in association with the unique subject identifier.

In particular embodiments, the system is configured to, for each stored unique subject identifier: (1) receive an indication that new personal data has been provided by or collected from a data subject associated with the unique subject identifier (e.g., provided to an entity or organization that collects and/or processes personal data); and (2) in response to receiving the indication, storing the new personal data (e.g., or storing an indication of a storage location of the new personal data by the entity) in association with the unique subject identifier. In this way, as an entity collects additional data for a particular unique data subject (e.g., having a unique subject identifier, hash, etc.), the third party data repository system is configured to maintain a centralized database of data collected, stored, and or processed for each unique data subject (e.g., indexed by unique subject identifier). The system may then, in response to receiving a data subject access request from a particular data subject, fulfill the request substantially automatically (e.g., by providing a copy of the personal data, deleting the personal data, indicating to the entity what personal data needs to be deleted from their system and where it is located, etc.). The system may, for example, automatically fulfill the request by: (1) identifying the unique subject identifier associated with the unique data subject making the request; and (2) retrieving any information associated with the unique data subject based on the unique subject identifier.

Exemplary Centralized Data Repository System Architecture

FIG. 36 is a block diagram of a centralized data repository system 3600 according to a particular embodiment. In various embodiments, the centralized data repository system 3600 is part of a privacy compliance system (also referred to as a privacy management system), or other system, which may, for example, be associated with a particular organization and be configured to aid in compliance with one or more legal or industry regulations related to the collection and storage of personal data. In any embodiment described herein, the centralized data repository system 3600 is a stand-alone system that is configured to interface with one or more first party data management or other systems for the purpose of maintaining a centralized data repository of personal data collected, stored, and/or processed by each of the one or more first party data systems.

As may be understood from FIG. 36, the centralized data repository system 3600 includes one or more computer networks 115, One or More Centralized Data Repository Servers 3610, a Consent Receipt Management Server 3620, One or More First Party System Servers 3630, One or More Databases 140 or other data structures, and one or more remote data subject computing devices 3650 (e.g., a desktop computer, laptop computer, tablet computer, smartphone, etc.). In particular embodiments, the One or More Centralized Data Repository Servers 3610, Consent Receipt Management Server 3620, One or More First Party System Servers 3630, One or More Databases 140 or other data structures, and one or more remote data subject computing devices 3650. Although in the embodiment shown in FIG. 36, the One or More Centralized Data Repository Servers 3610, Consent Receipt Management Server 3620, One or More First Party System Servers 3630, One or More Databases 140 or other data structures, and one or more remote data subject computing devices 3650 are shown as separate servers, it should be understood that in any embodiment described herein, one or more of these servers and/or computing devices may comprise a single server, a plurality of servers, one or more cloud-based servers, or any other suitable configuration.

In particular embodiments, the One or More Centralized Data Repository Servers 3610 may be configured to interface with the One or More First Party System Servers 3630 to receive any of the indications or personal data (e.g., for storage) described herein. The One or More Centralized Data Repository Servers 3610 and One or More First Party System Servers 3630 may, for example, interface via a suitable application programming interface, direct connection, etc. In a particular embodiment, the One or More Centralized Data Repository Servers 3610 comprise the Consent Receipt Management Server 3620.

In a particular example, a data subject may provide one or more pieces of personal data via the One or More Remote Data Subject Computing Devices 3650 to the One or More First Party System Servers 3630. The data subject may, for example, complete a webform on a website hosted on the One or More First Party System Servers 3630. The system may then, in response to receiving the one or more pieces of personal data at the One or More First Party System Servers 3630, transmit an indication to the One or More Centralized Data Repository Servers 3610 that the One or More First Party System Servers 3630 have collected, stored, and/or processed the one or more pieces of personal data. In response to receiving the indication, the One or More Centralized Data Repository Servers 3610 may then store the one or more pieces of personal data (e.g., a copy of the data, an indication of the storage location of the personal data in the One or More First Party System Servers 3630, etc.) in a centralized data storage location (e.g., in One or More Databases 140, on the One or More Centralized Data Repository Servers 3610, etc.).

Centralized Data Repository Module

Various functionality of the centralized data repository system 3600 may be implemented via a Centralized Data Repository Module 3700. The system, when executing certain steps of the Centralized Data Repository Module, may be configured to generate, a central repository of personal data on behalf of an entity, and populate the central repository with personal data as the entity collects, stores and/or processes the personal data. In particular embodiments, the system is configured to index the personal data within the central repository by data subject.

FIG. 37 depicts a Centralized Data Repository Module 3700 according to a particular embodiment. The system, when executing the Centralized Data Repository Module 3700, begins, at Step 3710, by receiving a request to generate a central repository of personal data on behalf of an entity. In particular embodiments, the system is a third-party system that receives a request from the entity to generate and maintain a central repository (e.g., third party repository) of personal data that the entity collects, stores, and or processes.

In particular embodiments, the system, in response to receiving the request, is configured to generate the central repository by: (1) designating at least a portion of one or more data stores for the storage of the personal data, information about the data subjects about whom the personal data is collected, etc.; (2) initiating a connection between the central repository and one or more data systems operated by the entity (e.g., one or more first party systems); (3) etc.

Continuing to Step 3720, the system is configured to generate, for each data subject about whom the entity collects, receives, and/or processes personal data, a unique identifier. The system may, for example: (1) receive an indication that a first party system has collected, stored, and/or processed a piece of personal data; (2) identify a data subject associated with the piece of personal data; (3) determine whether the central repository system is currently storing data associated with the data subject; and (4) in response to determining that the central repository system is not currently storing data associated with the data subject (e.g., because the data subject is a new data subject), generating the unique identifier. In various embodiments, the system is configured to assign a unique identifier for each data subject about whom the first party system has previously collected, stored, and/or processed personal data.

In particular embodiments, the unique identifier may include any unique identifier such as, for example: (1) any of the one or more pieces of personal data collected, stored, and/or processed by the system (e.g., name, first name, last name, full name, address, phone number, e-mail address, etc.); (2) a unique string or hash comprising any suitable number of numerals, letters, or combination thereof; and/or (3) any other identifier that is sufficiently unique to distinguish between a first and second data subject for the purpose of subsequent data retrieval.

In particular embodiments, the system is configured to assign a permanent identifier to each particular data subject. In any embodiment described herein, the system is configured to assign one or more temporary unique identifiers to the same data subject.

In particular embodiments, the unique identifier may be based at least in part on the unique receipt key and/or unique subject identifier discussed below with respect to the consent receipt management system. As may be understood in light of this disclosure, when receiving consent form a data subject to process, collect, and at least store one or more particular types of personal data associated with the data subject, the system is configured to generate a unique ID to memorialize the consent and provide authorization for the system to collect the subject's data. In any embodiment described herein, the system may be configured to utilize any unique ID generated for the purposes of tracking data subject consent as a unique identifier in the context of the central repository system described herein.

In particular embodiments, the system is configured to continue to Step 3730, and store the unique identifier in computer memory. In particular embodiments, the system is configured to store the unique identifier in an encrypted manner. In various embodiments, the system is configured to store the unique identifier in any suitable location (e.g., the one or more databases 140 described above).

In particular embodiments, the system is configured to store the unique identifier as a particular file structure such as, for example, a particular folder structure in which the system is configured to store one or more pieces of personal data (e.g., or pointers to one or more pieces of personal data) associated with the unique identifier (e.g., the data subject associated with the unique identifier). In any embodiment described herein, the system is configured to store the unique identifier in any other suitable manner (e.g., in a suitable data table, etc.).

Returning to Step 3740, the system is configured to receive an indication that one or more computer systems have received, collected or processed one or more pieces of personal data associated with a data subject. In particular embodiments, the one or more computer systems include any suitable computer system associated with a particular entity. In any embodiment described herein, the one or more computer systems comprise one or more software applications, data stores, databases, etc. that collect, process, and/or store data (e.g., personally identifiable data) on behalf of the entity (e.g., organization). In particular embodiments, the system is configured to receive the indication through integration with the one or more computer systems. In a particular example, the system may provide a software application for installation on a system device that is configured to transmit the indication in response to the system receiving, collecting, and/or processing one or more pieces of personal data.

In particular embodiments, the system may receive the indication in response to: (1) a first party system, data store, software application, etc. receiving, collecting, storing, and or processing a piece of data that includes personally identifying information; (2) a user registering for an account with a particular entity (e.g., an online account, employee account, social media account, e-mail account, etc.); (3) a company storing information about one or more data subjects (e.g., employee information, customer information, potential customer information, etc.; and/or (4) any other suitable indication that a first entity or any computer system or software on the first entity's behalf has collected, stored, and/or processed a piece of data that includes or may include personally identifiable information.

As a particular example, the system may receive the indication in response to a user submitting a webform via a website operated by the first entity. The webform may include, for example, one or more fields that include the user's e-mail address, billing address, shipping address, and payment information for the purposes of collected payment data to complete a checkout process on an e-commerce website. In this example, because the information submitted via the webform contains personal data (e.g., personally identifiable data) the system, in response to receiving an indication that the user has submitted the at least partially completed webform, may be configured to receive the indication described above with respect to Step 3740.

In various embodiments, a first party privacy management system or other system (e.g., privacy management system, marketing system, employee records database management system, etc.) may be configured to transmit an indication to the central repository system in response to collecting, receiving, or processing one or more pieces of personal data personal data.

In some embodiments, the indication may include, for example: (1) an indication of the type of personal data collected; (2) a purpose for which the personal data was collected; (3) a storage location of the personal data by the first party system; and/or (4) any other suitable information related to the one or more pieces of personal data or the handling of the personal data by the first party system. In particular embodiments, the system is configured to receive the indication via an application programming interface, a software application stored locally on a computing device within a network that makes up the first party system, or in any other suitable manner.

Continuing to Step 3750, the central repository system is configured to store, in computer memory, an indication of the personal data in association with the respective unique identifier. In various embodiments, the central repository system comprises a component of a first party system for the centralized storage of personal data collected by one or more various distributed computing systems (e.g., and software applications) operated by a particular entity for the purpose of collecting, storing, and/or processing personal data. In any embodiment described herein, the central repository system is a third-party data repository system that is separate from the one or more first party systems described above. In particular embodiments, for example, a third-party data repository system may be configured to maintain a central repository of personal data for a plurality of different entities.

In particular embodiments, the central repository system is configured to store a copy of the personal data (e.g., store a digital copy of the personal data in computer memory associated with the central repository system). In still any embodiment described herein, the central repository system is configured to store an indication of a storage location of the personal data within the first party system. For example, the system may be configured to store an indication of a physical location of a particular storage location (e.g., a physical location of a particular computer server or other data store) and an indication of a location of the personal data in memory on that particular storage location (e.g., a particular path or filename of the personal data, a particular location in a spreadsheet, CSV file, or other suitable document, etc.).

In various embodiments, the system may be configured to confirm receipt of valid consent to collect, store, and/or process personal data from the data subject prior to storing the indication of the personal data in association with the respective unique identifier. In such embodiments, the system may be configured to integrate with (e.g., interface with) a consent receipt management system (e.g., such as the consent receipt management system described more fully below). In such embodiments, the system may be configured to: (1) receive the indication that the first party system has collected, stored, and/or processed a piece of personal data; (2) identify, based at least in part on the piece of personal data, a data subject associated with the piece of personal data; (3) determine, based at least in part on one or more consent receipts received from the data subject (e.g., one or more valid receipt keys associated with the data subject), and one or more pieces of information associated with the piece of personal data, whether the data subject has provided valid consent to collect, store, and/or process the piece of personal data; (4) in response to determining that the data subject has provided valid consent, storing the piece of personal data in any manner described herein; and (5) in response to determining that the data subject has not provided valid consent, deleting the piece of personal data (e.g., not store the piece of personal data).

In particular embodiments, in response to determining that the data subject has not provided valid consent, the system may be further configured to: (1) automatically determine where the data subject's personal data is stored (e.g., by the first party system); and (2) in response to determining the location of the data (which may be on multiple computing systems), automatically facilitate the deletion of the data subject's personal data from the various systems (e.g., by automatically assigning a plurality of tasks to delete data across multiple business systems to effectively delete the data subject's personal data from the systems). In particular embodiments, the step of facilitating the deletion may comprise, for example: (1) overwriting the data in memory; (2) marking the data for overwrite; (2) marking the data as free (e.g., and deleting a directory entry associated with the data); and/or (3) any other suitable technique for deleting the personal data.

Next, at optional step 3760, the system is configured to take one or more actions based at least in part on the data stored in association with the unique identifier. In particular embodiments, the one or more actions may include, for example, responding to a data subject access request initiated by a data subject (e.g., or other individual on the data subject's behalf) associated with the unique identifier. In various embodiments, the system is configured to identify the unique identifier associated with the data subject making the data subject access request based on information submitted as part of the request.

Consent Receipt Management Systems

In particular embodiments, any entity (e.g., organization, company, etc.) that collects, stores, processes, etc. personal data may require one or more of: (1) consent from a data subject from whom the personal data is collected and/or processed; and/or (2) a lawful basis for the collection and/or processing of the personal data. In various embodiments, the entity may be required to, for example: (1) demonstrate that a data subject has freely given specific, informed, and unambiguous indication of the data subject's agreement to the processing of his or her personal data (e.g., in the form of a statement or clear affirmative action); (2) demonstrate that the entity received consent from a data subject in a manner clearly distinguishable from other matters (e.g., in an intelligible and easily accessible form, using clear and plain language, etc.); (3) enable a data subject to withdraw consent as easily as the data subject can give consent; (4) separate a data subject's consent from performance under any contract unless such processing is necessary for performance under the contract; etc.

In various embodiments, a consent receipt management system may be implemented in the context of any suitable privacy management system that is configured to ensure compliance with one or more legal or industry standards related to the collection and/or storage of private information (e.g., such as personal data). Various privacy and security policies (e.g., such as the European Union's General Data Protection Regulation, and other such policies) may provide data subjects (e.g., individuals, organizations, or other entities) with certain rights related to the data subject's personal data that is collected, stored, or otherwise processed by an organization. These rights may include, for example: (1) a right to erasure of the data subject's personal data (e.g., in cases where no legal basis applies to the processing and/or collection of the personal data; (2) a right to withdraw consent to the processing and/or collection of their personal data; (3) a right to receive the personal data concerning the data subject, which he or she has provided to an entity (e.g., organization), in a structured, commonly used and machine-readable format; and/or (4) any other right which may be afforded to the data subject under any applicable legal and/or industry policy.

In particular embodiments, the consent receipt management system is configured to: (1) enable an entity to demonstrate that valid consent has been obtained for each particular data subject for whom the entity collects and/or processes personal data; and (2) enable one or more data subjects to exercise one or more rights described herein.

The system may, for example, be configured to track data on behalf of an entity that collects and/or processes persona data related to: (1) who consented to the processing or collection of personal data (e.g., the data subject themselves or a person legally entitled to consent on their behalf such as a parent, guardian, etc.); (2) when the consent was given (e.g., a date and time); (3) what information was provided to the consenter at the time of consent (e.g., a privacy policy, what personal data would be collected following the provision of the consent, for what purpose that personal data would be collected, etc.); (4) how consent was received (e.g., one or more copies of a data capture form, webform, etc. via which consent was provided by the consenter); (5) when consent was withdrawn (e.g., a date and time of consent withdrawal if the consenter withdraws consent); and/or (6) any other suitable data related to receipt or withdrawal of consent.

In further embodiments, the system may be configured to provide data subjects with a centralized interface that is configured to: (1) provide information regarding each of one or more valid consents that the data subject has provided to one or more entities related to the collection and/or processing of their personal data; (2) provide one or more periodic reminders regarding the data subject's right to withdraw previously given consent (e.g., every 6 months in the case of communications data and metadata, etc.); (3) provide a withdrawal mechanism for the withdrawal of one or more previously provided valid consents (e.g., in a format that is substantially similar to a format in which the valid consent was given by the data subject); (4) refresh consent when appropriate (e.g., the system may be configured to elicit updated consent in cases where particular previously validly consented to processing is used for a new purpose, a particular amount of time has elapsed since consent was given, etc.).

In particular embodiments, the system is configured to manage one or more consent receipts between a data subject and an entity. In various embodiments, a consent receipt may include a record (e.g., a data record stored in memory and associated with the data subject) of consent, for example, as a transactional agreement where the data subject is already identified or identifiable as part of the data processing that results from the provided consent. In any embodiment described herein, the system may be configured to generate a consent receipt in response to a data subject providing valid consent. In some embodiments, the system is configured to determine whether one or more conditions for valid consent have been met prior to generating the consent receipt.

Exemplary Consent Receipt Data Flow

FIG. 38 depicts an exemplary data flow that a consent receipt management system may utilize in the recordation and management of one or more consent receipts. In particular embodiments, a third-party consent receipt management system may be configured to manage one or more consent receipts for a particular entity. As may be understood from this figure, a data subject may access an interaction interface (e.g., via the web) for interacting with a particular entity (e.g., one or more entity systems). The interaction interface (e.g., user interface) may include, for example, a suitable website, web form, user interface etc. The interaction interface may be provided by the entity. Using the interaction interface, a data subject may initiate a transaction with the entity that requires the data subject to provide valid consent (e.g., because the transaction includes the processing of personal data by the entity). The transaction may include, for example: (1) accessing the entity's website; (2) signing up for a user account with the entity; (3) signing up for a mailing list with the entity; (4) a free trial sign up; (5) product registration; and/or (6) any other suitable transaction that may result in collection and/or processing personal data, by the entity, about the data subject.

As may be understood from this disclosure, any particular transaction may record and/or require one or more valid consents from the data subject. For example, the system may require a particular data subject to provide consent for each particular type of personal data that will be collected as part of the transaction. The system may, in various embodiments, be configured to prompt the data subject to provide valid consent, for example, by: (1) displaying, via the interaction interface, one or more pieces of information regarding the consent (e.g., what personal data will be collected, how it will be used, etc.); and (2) prompt the data subject to provide the consent.

In response to the data subject (e.g., or the entity) initiating the transaction, the system may be configured to: (1) generate a unique receipt key (e.g., unique receipt ID); (2) associate the unique receipt key with the data subject (e.g., a unique subject identifier), the entity, and the transaction; and (3) electronically store (e.g., in computer memory) the unique receipt key. The system may further store a unique user ID (e.g., unique subject identifier) associated with the data subject (e.g., a hashed user ID, a unique user ID provided by the data subject, unique ID based on a piece of personal data such as an e-mail address, etc.).

In a particular embodiment, the unique consent receipt key is generated by a third-party consent receipt management system. The system may then be configured to associate the unique consent receipt key with the interaction interface, and further configured to associate the unique consent receipt key with a unique transaction ID generated as a result of a data subject transaction initiated via the interaction interface.

In particular embodiments, the unique consent receipt key may be associated with one or more receipt definitions, which may include, for example: (1) the unique transaction ID; (2) an identity of one or more controllers and/or representatives of the entity that is engaging in the transaction with the data subject (e.g., and contact information for the one or more controllers); (3) one or more links to a privacy policy associated with the transaction at the time that consent was given; (4) a listing of one or more data types for which consent to process was provided (e.g., email, MAC address, name, phone number, browsing history, etc.); (5) one or more methods used to collect data for which consent to process was provided (e.g., using one or more cookies, receiving the personal data from the data subject directly, etc.); (6) a description of a service (e.g., a service provided as part of the transaction such as a free trial, user account, etc.); (7) one or more purposes of the processing (e.g., for marketing purposes, to facilitate contact with the data subject, etc.); (8) a jurisdiction (e.g., the European Union, United States, etc.); (9) a legal basis for the collection of personal data (e.g., consent); (10) a type of consent provided by the data subject (e.g. unambiguous, explicit, etc.); (11) one or more categories or identities of other entities to whom the personal data may be transferred; (12) one or more bases of a transfer to a third party entity (e.g., adequacy, binding corporate rules, etc.); (13) a retention period for the personal data (e.g., how long the personal data will be stored); (14) a withdrawal mechanism (e.g., a link to a withdrawal mechanism); (15) a timestamp (e.g., date and time); (16) a unique identifier for the receipt; and/or (17) any other suitable information. FIG. 39 depicts an exemplary consent definition summary for a particular transaction (e.g., free trial signup).

In response to receiving valid consent from the data subject, the system is configured to transmit the unique transaction ID and the unique consent receipt key back to the third-party consent receipt management system for processing and/or storage. In any embodiment described herein, the system is configured to transmit the transaction ID to a data store associated with one or more entity systems (e.g., for a particular entity on behalf of whom the third-party consent receipt management system is obtaining and managing validly received consent). In further embodiments, the system is configured to transmit the unique transaction ID, the unique consent receipt key, and any other suitable information related to the validly given consent to the centralized data repository system described above for use in determining whether to store particular data and/or for assigning a unique identifier to a particular data subject for centralized data repository management purposes.

The system may be further configured to transmit a consent receipt to the data subject which may include, for example: (1) the unique transaction ID; (2) the unique consent receipt key; and/or (3) any other suitable data related to the validly provided consent. In some embodiments, the system is configured to transmit a consent receipt in any suitable format (e.g., JSON, HTML, e-mail, text, cookie, etc.). In particular embodiments, the receipt transmitted to the data subject may include a link to a subject rights portal via which the data subject may, for example: (1) view one or more provided valid consents; (2) withdraw consent; (3) etc.

Exemplary Data Subject Consent Receipt User Experience

FIGS. 40 and 41 depict exemplary screen displays that a data subject may encounter when providing consent to the processing of personal data. As shown in FIG. 40, a data subject (e.g., John Doe) may provide particular personal data (e.g., first and last name, email, company, job title, phone number, etc.) when signing up for a free trial with a particular entity via a trial signup interface 4000. As may be understood in light of this disclosure, the free trial may constitute a transaction between the data subject (e.g., user) and a particular entity providing the free trial. In various embodiments, the data subject (e.g., user) may encounter the interface shown in FIG. 40 in response to accessing a website associated with the particular entity for the free trial (e.g., a sign-up page).

In particular embodiments, the interface 4000 is configured to enable the user (e.g., data subject) to provide the information required to sign up for the free trial. As shown in FIG. 40, the interface further includes a listing of particular things that the data subject is consenting to (e.g., the processing of first name, last name, work email, company, job title, and phone number) as well as one or more purposes for the processing of such data (e.g., marketing information). The interface further includes a link to a Privacy Policy that governs the use of the information.

In various embodiments, in response to the user (e.g., data subject) submitting the webform shown in FIG. 40, the system is configured to generate a consent receipt that memorializes the user's provision of the consent (e.g., by virtue of the user submitting the form). FIG. 41 depicts an exemplary consent receipt 4100 in the form of a message transmitted to the data subject (e.g., via e-mail). As shown in this figure, the consent receipt includes, for example: (1) a receipt number (e.g., a hash, key, or other unique identifier); (2) what information was processed as a result of the user's consent (e.g., first and last name, email, company, job title, phone number, etc.); (3) one or more purposes of the processing (e.g., marketing information); (4) information regarding withdrawal of consent; (5) a link to withdraw consent; and (6) a timestamp at which the system received the consent (e.g., a time at which the user submitted the form in FIG. 40). In any embodiment described herein, the consent receipt transmitted to the user may include any other suitable information.

FIG. 42 depicts an exemplary log of consent receipts 4200 for a particular transaction (e.g., the free trial signup described above). As shown in this figure, the system is configured to maintain a database of consent receipts that includes, for example, a timestamp of each receipt, a unique key associated with each receipt, a customer ID associated with each receipt (e.g., the customer's e-mail address), etc. In particular embodiments, the centralized data repository system described above may be configured to cross-reference the database of consent receipts (e.g., or maintain the database) in response to receiving the indication that a first party system has received, stored, and/or processed personal data (e.g., via the free trial signup interface) in order to confirm that the data subject has provided valid consent prior to storing the indication of the personal data.

Exemplary Transaction Creation User Experience

FIGS. 43-54 depict exemplary user interfaces via which a user (e.g., a controller or other individual associated with a particular entity) may create a new transaction for which the system is configured to generate a new interaction interface (e.g., interface via which the system is configured to elicit and receive consent for the collection and/or processing of personal data from a data subject under the new transaction.

As shown in FIG. 43, the system is configured to display a dashboard of existing transactions 4300 that are associated with a particular entity. In the example shown in this figure, the dashboard includes, for example: (1) a name of each transaction; (2) a status of each transaction; (2) one or more data categories collected as part of each transaction; (3) a unique subject ID used as part of the transaction (e.g., email, device ID, etc.); (4) a creation date of each transaction; (5) a date of first consent receipt under each transaction; and (6) a total number of receipts received for each transaction. The dashboard further includes a Create New Transaction button, which a user may select in order to create a new transaction.

As may be understood in light of this disclosure, in various embodiments, the centralized data repository system described above may limit storage of personal data on behalf of a particular entity to specific personal data for which the particular entity has received consent from particular data subjects. Based on the exemplary dashboard of existing transactions shown in FIG. 43, for example, the system may be configured to not store any personal data collected, and/or processed other than in response to an indication that the data was collected through the free trial signup or product registration transaction.

FIG. 44 depicts an interface 4400 for creating a new transaction, which a user may access, for example, by selecting the Create New Transaction button shown in FIG. 43. As may be understood from this figure, when creating a new transaction, the user may enter, via one or more text entry forms, a name of the transaction, a description of the transaction, a group associated with the transaction, and/or any other suitable information related to the new transaction.

Continuing to FIG. 45, the system may be configured to prompt the user to select whether the new transaction is based on an existing processing activity. An existing processing activity may include, for example, any other suitable transaction or any other activity that involves the collection and/or processing of personal data. In response to the user selecting that the new transaction is not related to an existing processing activity (e.g., as shown in FIG. 45), the system may be configured to prompt the user, via one or more additional interfaces, to provide information regarding the new transaction.

FIGS. 47-54 depict exemplary user interfaces via which the user may provide additional information regarding the new transaction. In various embodiments, the system may be configured to prompt the user to provide the information via free-form text entry, via one or more drop down menus, by selecting one or more predefined selections, or in any suitable manner. In some embodiments, the system is configured to prompt the user to provide one or more standardized pieces of information regarding the new transaction. In any embodiment described herein, the system is configured to enable a particular entity (e.g., organization, company, etc.) to customize one or more questions or prompts that the system displays to a user creating a new transaction.

As shown in FIG. 46, the system may, for example, prompt the user, via the user interface, to: (1) describe a process or service that the consent under the transaction relates to; (2) provide a public URL where consent is or will be collected; (3) provide information regarding how consent is being collected (e.g., via a website, application, device, paper form, etc.); (4) provide information regarding one or more data elements that will be processed based on the consent provided by the data subject (e.g., what particular personal data will be collected); and (5) provide information regarding what data elements are processed by one or more background checks (e.g., credit check and/or criminal history).

Continuing to FIG. 47, the system may be configured to prompt the user to provide data related to, for example: (1) one or more elements that will be used to uniquely identify a data subject; (2) a purpose for seeking consent; (3) what type of consent is sought (e.g., unambiguous, explicit, not sure, etc.); (4) who is the data controller in charge of the processing of the personal data (e.g., the legal entity responsible); (5) a contact address (e.g., for the data controller; (6) etc.

As shown in FIG. 48, the system may be further configured to prompt the user to provide data regarding, for example: (1) who the contact person is for the transaction (e.g., a job title, name, etc. of the contact person); (2) a contact email (e.g., an email address that a data subject can contact to get more information about the transaction, consent, etc.); (3) a contact telephone number (e.g., a telephone number that a data subject can contact to get more information about the transaction, consent, etc.); (4) an applicable jurisdiction for the processing (e.g., European Union, United States, Other, etc.), which may include one or more jurisdictions; (5) a URL of a privacy policy associated with the transaction; (6) etc.

Next, as shown in FIG. 49, the system may be further configured to prompt the user to provide data regarding: (1) whether the personal data will be shared with one or more third parties; (2) a name of the one or more third parties; (3) whether the processing of the personal data will involve a transfer of the personal data outside of the original jurisdiction; (4) a listing of one or more destination countries, regions, or other jurisdictions that will be involved in any international transfer; (5) a process for a data subject to withdraw consent; (6) a URL for the withdrawal mechanism; (7) etc. FIG. 50 depicts a user interface that includes additional data prompts for the user to respond to regarding the new transaction. As shown in FIG. 50, the system may be further configured to prompt the user to provide data regarding, for example: (1) what the retention period is for the personal data (e.g., how long the personal data will be stored in identifiable form, a period before anonymization of the personal data, etc.); and/or (2) a life span of the consent (e.g., a period of time during which the consent is assumed to be valid).

FIG. 51 shows an exemplary user interface for selecting a processing activity in response to the user indicating that the new transaction is based on an existing processing activity. The user may, for example, use a drop-down menu to select a suitable existing processing activity. In particular embodiments, the system is configured to populate the drop-down menu with one or more processing activities from a data model associated with the processing activity. The system may then be configured to substantially automatically populate one or more responses to the questions described above based at least in part on the data model (e.g., automatically include particular data elements collected as part of the processing activity, etc.).

In particular embodiments, the system is further configured to enable a controller (e.g., or other user on behalf of the entity) to search for one or more consent receipts received for a particular data subject (e.g., via a unique subject identifier). FIG. 52 depicts a search for a unique subject identifier that includes an e-mail address. As shown in this figure, the unique subject identifier (e.g., john.doe@gmail.com) has one associated consent receipt having a receipt number, a receipt date and time, and a withdrawal date. FIG. 53 depicts an additional exemplary search results page indicating one or more results for consent receipts associated with the unique subject identifier of john.doe@gmail.com. As shown in this figure, the system may be configured to display a process name (e.g., transaction name), receipt number, consent date, status, withdrawal date, and other suitable information for one or more consent receipts associated with the searched for unique subject identifier.

As may be understood in light of this disclosure, in response to a user creating a new transaction, the system may be configured to generate a web form, web page, piece of computer code, etc. for the collection of consent by a data subject as part of the new transaction. FIG. 54 depicts an exemplary dashboard of consent receipt management implementation code which the system may automatically generate for the implementation of a consent receipt management system for a particular transaction. As shown in this figure, the system displays particular computer code (e.g., in one or more different programming language) that the system has generated. A user may place the generated code on a webpage or other location that the user desires to collect consent.

Exemplary Consent Receipt Management System Architecture

FIG. 55 is a block diagram of a Consent Receipt Management System 5500 according to a particular embodiment. In some embodiments, the Consent Receipt Management System 5500 is configured to interface with at least a portion of each respective organization's Privacy Compliance System in order generate, capture, and maintain a record of one or more consents to process, collect, and or store personal data from one or more data subjects.

As may be understood from FIG. 55, the Consent Receipt Management System 5500 includes one or more computer networks 115, a Consent Receipt Management Server 5510, a Consent Receipt Capture Server 5520 (e.g., which may be configured to run one or more virtual browsers 5525 as described herein), One or More Consent Web Form Hosting Servers 5530, one or more databases 140, and one or more remote computing devices 5550 (e.g., a desktop computer, laptop computer, tablet computer, etc.). In particular embodiments, the one or more computer networks 115 facilitate communication between the Consent Receipt Management Server 5510, a Consent Receipt Capture Server 5520, One or More Consent Web Form Hosting Servers 5530, one or more databases 140, and one or more remote computing devices 5550.

The one or more computer networks 115 may include any of a variety of types of wired or wireless computer networks such as the Internet, a private intranet, a public switch telephone network (PSTN), or any other type of network. The communication link between Consent Receipt Capture Server 5520 and Database 140 may be, for example, implemented via a Local Area Network (LAN) or via the Internet.

Exemplary Consent Receipt Management System Platform

Various embodiments of a Consent Receipt Management System 5500 4500 may be implemented in the context of any suitable system (e.g., a privacy compliance system). For example, the Consent Receipt Management System 5500 may be implemented to facilitate receipt and maintenance of one or more valid consents provided by one or more data subjects for the processing and/or at least temporary storage of personal data associated with the data subjects. In particular embodiments, the system may implement one or more modules in order to at least partially ensure compliance with one or more regulations (e.g., legal requirements) related to the collection and/or storage of personal data. Various aspects of the system's functionality may be executed by certain system modules, including a Consent Receipt Management Module 5600, a Consent Expiration and Re-Triggering Module 5700, and a Consent Validity Scoring Module 5900. These modules are discussed in greater detail below.

Although the system may be configured to execute the functions described in the modules as a series of steps, it should be understood in light of this disclosure that various embodiments of the Consent Receipt Management Module 5600, Consent Expiration and Re-Triggering Module 5700, and Consent Validity Scoring Module 5900 described herein may perform the steps described below in an order other than in which they are presented. In still any embodiment described herein, the Consent Receipt Management Module 5600, Consent Expiration and Re-Triggering Module 5700, and Consent Validity Scoring Module 5900 may omit certain steps described below. In any embodiment described herein, the Consent Receipt Management Module 5600, Consent Expiration and Re-Triggering Module 5700, and Consent Validity Scoring Module 5900 may perform steps in addition to those described (e.g., such as one or more steps described with respect to one or more other modules, etc.).

Consent Receipt Generation

In various embodiments, a consent receipt management system is configured to generate a consent receipt for a data subject that links to (e.g., in computer memory) metadata identifying a particular purpose of the collection and/or processing of personal data that the data subject consented to, a capture point of the consent (e.g., a copy of the web form or other mechanism through which the data subject provided consent, and other data associated with one or more ways in which the data subject granted consent.

The system may, for example, be configured to track data on behalf of an entity that collects and/or processes persona data related to: (1) who consented to the processing or collection of personal data (e.g., the data subject themselves or a person legally entitled to consent on their behalf such as a parent, guardian, etc.); (2) when the consent was given (e.g., a date and time); (3) what information was provided to the consenter at the time of consent (e.g., a privacy policy, what personal data would be collected following the provision of the consent, for what purpose that personal data would be collected, etc.); (4) how consent was received (e.g., one or more copies of a data capture form, web form, etc. via which consent was provided by the consenter); (5) when consent was withdrawn (e.g., a date and time of consent withdrawal if the consenter withdraws consent); and/or (6) any other suitable data related to receipt or withdrawal of consent.

Using an interaction interface, a data subject may initiate a transaction with the entity that requires the data subject to provide valid consent (e.g., because the transaction includes the processing of personal data by the entity). The transaction may include, for example: (1) accessing the entity's website (e.g., which may utilize one or more cookies and/or other tracking technologies to monitor the data subject's activity while accessing the website or other websites; enable certain functionality on one or more pages of the entity's website, such as location services; etc.); (2) signing up for a user account with the entity; (3) signing up for a mailing list with the entity; (4) a free trial sign up; (5) product registration; and/or (6) any other suitable transaction that may result in collection and/or processing of personal data, by the entity, about the data subject.

As may be understood from this disclosure, any particular transaction may record and/or require one or more valid consents from the data subject. For example, the system may require a particular data subject to provide consent for each particular type of personal data that will be collected as part of the transaction. The system may, in various embodiments, be configured to prompt the data subject to provide valid consent, for example, by: (1) displaying, via the interaction interface, one or more pieces of information regarding the consent (e.g., what personal data will be collected, how it will be used, etc.); and (2) prompt the data subject to provide the consent.

In response to the data subject (e.g., or the entity) initiating the transaction, the system may be configured to: (1) generate a unique receipt key (e.g., unique receipt ID); (2) associate the unique receipt key with the data subject (e.g., via a unique subject identifier), the entity, and the transaction; and (3) electronically store (e.g., in computer memory) the unique receipt key. The system may further store a unique user ID (e.g., unique subject identifier) associated with the data subject (e.g., a hashed user ID, a unique user ID provided by the data subject, unique ID based on a piece of personal data such as an e-mail address, etc.). In any embodiment described herein, the system may be configured to store computer code associated with the capture of the consent by the system. The system may, for example, store computer code associated with a web form or other consent capture mechanism. In any embodiment described herein, the system is configured to capture one or more images of one or more webpages via which a data subject provides (e.g., provided) consent (e.g., substantially at the time at which the data subject provided consent). This may, for example, enable an entity or other organization to demonstrate one or more conditions under which consent was received for a particular data subject in order to comply with one or more regulations related to the securing of consent.

In a particular embodiment, the system is configured to: (1) use a virtual web browser to access a URL via which a data subject provided consent for a particular processing activity or other transaction; (2) capture one or more images of one or more web sites at the URL, the one or more images containing one or more web forms or other portions of the one or more web pages via which the data subject provided one or more inputs that demonstrated the data subject's consent; and store the one or more images in association with metadata associated with one or more consent receipts related to the received consent. In some embodiments, the system may be configured to: (1) scan, via the virtual web browser, a particular website and/or URL; (2) identify a web form at the particular website and/or URL; and (3) capture one or more images (e.g., screenshots) of the web form (e.g., in an unfilled-out state). In some embodiments, the system is configured to use a virtual web browser that corresponds to a web browser via which the user completed the web form. For example, the system may be configured to identify a particular web browser utilized by the data subject and initiate the virtual browsing session using the identified web browser.

FIG. 56 depicts an exemplary Consent Receipt Management Module 5600 that includes steps that the system may execute in order to generate a consent receipt. As may be understood from FIG. 56, the system may be configured to: (1) provide a user interface for initiating a transaction between an entity and a data subject (e.g., such as a web form via which the data subject may authorize or consent to the processing, collection, or storage of personal data associated with the transaction) at Step S610; (2) receive a request to initiate a transaction between the entity and the data subject (e.g., from a computing device associated with the data subject via a web form located at a particular URL, on a particular webpage, etc.) at Step S620; (3) in response to receiving the request, generating, by a third party consent receipt management system, a unique consent receipt key at Step S630; (4) in response to receiving the request, initiating a virtual browsing session on a second computing device (e.g., a second computing device associated with the third party consent receipt management system) at Step S630; (5) using the virtual browser to access the particular URL or particular webpage that hosts the web form at Step S640; (6) capturing, via the virtual browser, one or more images of the web form, the URL, and/or the particular webpage at Step S650; (7) store a unique subject identifier associated with the data subject, the unique consent receipt key, a unique transaction identifier associated with the transaction, and the one or more images in computer memory at Step S660; and (8) electronically associating the unique subject identifier, the unique consent receipt key, the unique transaction identifier, and the one or more images.

FIG. 40 depicts an exemplary screen display that a data subject may encounter when providing consent to the processing of personal data. As shown in FIG. 40, a data subject (e.g., John Doe) may provide particular personal data (e.g., first and last name, email, company, job title, phone number, etc.) when signing up for a free trial with a particular entity. As may be understood in light of this disclosure, the free trial may constitute a transaction between the data subject (e.g., user) and a particular entity providing the free trial. In various embodiments, the data subject (e.g., user) may encounter the interface shown in FIG. 40 in response to accessing a website associated with the particular entity for the free trial (e.g., a sign-up page).

In particular embodiments, the interface is configured to enable the user (e.g., data subject) to provide the information required to sign up for the free trial. As shown in FIG. 40, the interface further includes a listing of particular things that the data subject is consenting to (e.g., the processing of first name, last name, work email, company, job title, and phone number) as well as one or more purposes for the processing of such data (e.g., marketing information). The interface further includes a link to a Privacy Policy that governs the use of the information.

In various embodiments, in response to the user (e.g., data subject) submitting the webform shown in FIG. 40, the system is configured to generate a consent receipt that memorializes the user's provision of the consent (e.g., by virtue of the user submitting the form). FIG. 40 depicts an uncompleted version of the web form from FIG. 40 that the system may capture via a virtual browsing session described herein and store in association with the consent receipt. FIG. 41 depicts an exemplary consent receipt in the form of a message transmitted to the data subject (e.g., via e-mail). As shown in this figure, the consent receipt includes, for example: (1) a receipt number (e.g., a hash, key, or other unique identifier); (2) what information was processed as a result of the user's consent (e.g., first and last name, email, company, job title, phone number, etc.); (3) one or more purposes of the processing (e.g., marketing information); (4) information regarding withdrawal of consent; (5) a link to withdraw consent; and (6) a timestamp at which the system received the consent (e.g., a time at which the user submitted the form in FIG. 2). In any embodiment described herein, the consent receipt transmitted to the user may include any other suitable information (e.g., such as a link to an unfilled out version of the web form via which the user provided consent, etc.)

In particular embodiments, the system is configured to generate a code associated with a particular web form. The system may then associate the code with a particular website, mobile application, or other location that hosts the web form.

In any embodiment described herein, the system is configured to capture one or more images (e.g., and/or one or more copies) of one or more privacy policies and/or privacy notices associated with the transaction or processing activity. This may include, for example, one or more privacy policies and/or privacy notices that dictate one or more terms under which the data subject provided consent (e.g., consent to have personal data associated with the data subject processed, collected, and/or stored). The system may be further configured to store and associate the captured one or more privacy policies and/or privacy notices with one or more of the unique subject identifiers, the unique consent receipt key, the unique transaction identifier, etc.

In various embodiments, the system is configured to generate a web form for use by an entity to capture consent from one or more data subjects. In any embodiment described herein, the system is configured to integrate with an existing web form. The system may, for example, be configured to record each particular selection and/or text entry by the data subject via the web form and capture (e.g., via the virtual browsing session described above) one or more images (e.g., screenshots) which may demonstrate what the web form looked like at the time the consent was provided (e.g., in an unfilled out state).

As may be understood in light of this disclosure, in response to a user creating a new transaction on behalf of an entity, the system may be configured to generate a web form, web page, piece of computer code, etc. for the collection of consent by a data subject as part of the new transaction. FIG. 54 depicts an exemplary dashboard of consent receipt management implementation code which the system may automatically generate for the implementation of a consent receipt management system for a particular transaction. As shown in this figure, the system displays particular computer code (e.g., in one or more different programming language) that the system has generated. A user may place the generated code on a webpage, within a mobile application, or other location that the user desires to collect consent.

In some embodiments, the system is configured to capture and store the underlying code for a particular web form (e.g., HTML, or other suitable computer code), which may, for example, be used to demonstrate how the consent from the data subject was captured at the time of the capture. In some embodiments, the system may be configured to capture the underlying code via the virtual browsing session described above.

In particular embodiments, the system is configured to enable an entity to track one or more consent provisions or revocations received via one or more venues other than via a computing device. For example, a data subject may provide or revoke consent via: (1) a phone call; (2) via paper (e.g., paper mailing); and/or (3) any other suitable avenue. The system may, for example, provide an interface via which a customer support representation can log a phone call from a data subject (e.g., a recording of the phone call) and generate a receipt indicating that the call occurred, what was requested on the call, whether the request was fulfilled, and a recording of the call. Similarly, the system may be configured to provide an interface to scan or capture one or more images of one or more consents provided or revoked via mail (e.g., snail mail).

Consent Receipts—Automatic Expiration and Triggering of Consent Recapture

In particular embodiments, the consent receipt management system is configured to: (1) automatically cause a prior, validly received consent to expire (e.g., in response to a triggering event); and (2) in response to causing the previously received consent to expire, automatically trigger a recapture of consent. In particular embodiments, the system may, for example, be configured to cause a prior, validly received consent to expire in response to one or more triggering events such as: (1) a passage of a particular amount of time since the system received the valid consent (e.g., a particular number of days, weeks, months, etc.); (2) one or more changes to a purpose of the data collection for which consent was received (e.g., or one or more other changes to one or more conditions under which the consent was received; (3) one or more changes to a privacy policy associated with the consent; (3) one or more changes to one or more rules (e.g., laws, regulations, etc.) that govern the collection or demonstration of validly received consent; and/or (4) any other suitable triggering event or combination of events. In particular embodiments, such as any embodiment described herein, the system may be configured to link a particular consent received from a data subject to a particular version of a privacy policy, to a particular version of a web form through which the data subject provided the consent, etc. The system may then be configured to detect one or more changes to the underlying privacy policy, consent receipt methodology, etc., and, in response, automatically expire one or more consents provided by one or more data subjects under a previous version of the privacy policy or consent capture form.

In various embodiments, the system may be configured to substantially automatically expire a particular data subject's prior provided consent in response to a change in location of the data subject. The system may, for example, determine that a data subject is currently located in a jurisdiction, country, or other geographic location other than the location in which the data subject provided consent for the collection and/or processing of their personal data. The system may be configured to determine that the data subject is in a new location based at least in part on, for example, a geolocation (e.g., GPS location) of a mobile computing device associated with the data subject, an IP address of one or more computing devices associated with the data subject, etc.). As may be understood in light of this disclosure, one or more different countries, jurisdictions, etc. may impose different rules, regulations, etc. related to the collection, storage, and processing of personal data. As such, in response to a user moving to a new location (e.g., or in response to a user temporarily being present in a new location), the system may be configured to trigger a recapture of consent based on one or more differences between one or more rules or regulations in the new location and the original location from which the data subject provided consent. In some embodiments, the system may substantially automatically compare the one or more rules and/or regulations of the new and original locations to determine whether a recapture of consent is necessary.

In particular embodiments, in response to the automatic expiration of consent, the system may be configured to automatically trigger a recapture of consent (e.g., based on the triggering event). The system may, for example, prompt the data subject to re-provide consent using, for example: (1) an updated version of the relevant privacy policy; (2) an updated web form that provides one or more new purposes for the collection of particular personal data; (3) one or more web forms or other consent capture methodologies that comply with one or more changes to one or more legal, industry, or other regulations; and/or (4) etc.

FIG. 57 depicts an exemplary Consent Expiration and Re-Triggering Module 5700 according to a particular embodiment. In various embodiments, when executing the Consent Expiration and Re-Triggering Module 5700, the system is configured to, beginning at Step S710, by determining that a triggering event has occurred. In various embodiments, the triggering event may include nay suitable triggering event such as, for example: (1) passage of a particular amount of time since a valid consent was received; (2) determination that a data subject for which the system has previously received consent is now located in a new jurisdiction, country, geographic location, etc.; (3) a change to one or more uses of data for which the data subject provided consent for the collection and/or processing; (4) a change to one or more privacy policies; and/or (5) any other suitable triggering event related to one or more consents received by the system.

Continuing to Step S720, the system is configured to cause an expiration of at least one validly received consent in response to determining that the triggering event has occurred. In response to causing the expiration of the at least one consent, the system may be configured to cease processing, collecting, and/or storing personal data associated with the prior provided consent (e.g., that has now expired). The system may then, at Step S730, in response to causing the expiration of the at least one validly received consent, automatically trigger a recapture of the at least one expired consent.

Consent Preference Modification Capture Systems

In particular embodiments, the consent receipt management system is configured to provide a centralized repository of consent receipt preferences for a plurality of data subjects. In various embodiments, the system is configured to provide an interface to the plurality of data subjects for modifying consent preferences and capture consent preference changes. The system may provide the ability to track the consent status of pending and confirmed consents. In any embodiment described herein, the system may provide a centralized repository of consent receipts that a third-party system may reference when taking one or more actions related to a processing activity. For example, a particular entity may provide a newsletter that one or more data subjects have consented to receiving. Each of the one or more data subjects may have different preferences related to how frequently they would like to receive the newsletter, etc. In particular embodiments, the consent receipt management system may receive a request from a third-party system to transmit the newsletter to the plurality of data subjects. The system may then cross-reference an updated consent database to determine which of the data subjects have a current consent to receive the newsletter, and whether transmitting the newsletter would conflict with any of those data subjects' particular frequency preferences. The system may then be configured to transmit the newsletter to the appropriate identified data subjects.

In particular embodiments, the system may be configured to identify particular consents requiring a double opt-in (e.g., an initial consent followed by a confirmatory consent in respond to generation of an initial consent receipt in order for consent to be valid). In particular embodiments, the system may track consents with a “half opt-in” consent status and take one or more steps to complete the consent (e.g., one or more steps described below with respect to consent conversion analytics).

The system may also, in particular embodiments, proactively modify subscriptions or other preferences for users in similar demographics based on machine learning of other users in that demographic opting to make such modifications. For example, the system may be configured to modify a user's preferences related to a subscription frequency for a newsletter or make other modifications in response to determining that one or more similarly situated data subjects (e.g., subjects of similar age, gender, occupation, etc.) have mad such modifications. In various embodiments, the system may be configured to increase a number of data subjects that maintain consent to particular processing activities while ensuring that the entity undertaking the processing activities complies with one or more regulations that apply to the processing activities.

Consent Conversion Analytics

In particular embodiments, a consent receipt management system is configured to track and analyze one or more attributes of a user interface via which data subjects are requested to provide consent (e.g., consent to process, collect, and/or store personal data) in order to determine which of the one or more attributes are more likely to result in a successful receipt of consent from a data subject. For example, the system may be configured to analyze one or more instances in which one or more data subjects provided or did not provide consent in order to identify particular attributes and/or factors that may increase a likelihood of a data subject providing consent. The one or more attributes may include, for example: (1) a time of day at which particular data subjects provided/did not provide consent; (2) a length of an e-mail requesting consent in response to which particular data subjects provided/did not provide consent; (3) a number of e-mails requesting consent in a particular time period sent to particular data subjects in response to at least one of which particular data subjects provided/did not provide consent; (4) how purpose-specific a particular email requesting consent was; (5) whether an e-mail requesting consent provided one or more opt-down options (e.g., one or more options to consent to receive a newsletter less frequently); (5) whether the e-mail requesting consent included an offer; (6) how compelling the offer was; (7) etc. The system may then aggregate these analyzed attributes and whether specific attributes increased or decreased a likelihood that a particular data subject may provide consent and use the aggregated analysis to automatically design a user interface, e-mail message, etc. that is configured to maximize consent receipt conversion based on the analytics.

In particular embodiments, the system may further be configured to generate a customized interface or message requesting consent for a particular data subject based at least in part on an analysis of similarly situated data subjects that provided consent based on particular attributes of an e-mail message or interface via which the consent was provided. For example, the system may identify one or more similarly situated data subjects based at least in part on: (1) age; (2) gender; (3) occupation; (4) income level; (5) interests, etc. In particular embodiments, a male between the ages of 18-25 may, for example, respond to a request for consent with a first set of attributes more favorably than a woman between the ages of 45 and 50 (e.g., who may respond more favorably to a second set of attributes).

The system may be configured to analyze a complete consent journey (e.g., from initial consent, to consent confirmation in cases where a double opt-in is required to validly receive consent). In particular embodiments, the system is configured to design interfaces particularly to capture the second step of a double opt-in consent or to recapture consent in response to a change in conditions under which consent was initially provided.

In particular embodiments, the system may be configured to use the analytics described herein to determine a particular layout, interaction, time of day, number of e-mails, etc. cause the highest conversion rate across a plurality of data subjects (e.g., across a plurality of similarly situated data subjects of a similar demographic).

FIG. 58 depicts an exemplary consent conversion analysis interface. As may be understood from this figure, the system may be configured to track, for example: (1) total unique visitors to a particular website (e.g., to which the system may attempt to obtain consent for particular data processing); (2) overall opt-in percentage of consent; (3) opt-in percent by actions; (4) opt-out percentage by actions, etc.

Consent Validity Scoring Systems

In particular embodiments, a consent receipt management system may include one or more consent validity scoring systems. In various embodiments, a consent validity scoring system may be configured to detect a likelihood that a user is correctly consenting via a web form. The system may be configured to determine such a likelihood based at least in part on one or more data subject behaviors while the data subject is completing the web form in order to provide consent. In various embodiments, the system is configured to monitor the data subject behavior based on, for example: (1) mouse speed; (2) mouse hovering; (3) mouse position; (4) keyboard inputs; (5) an amount of time spent completing the web form; and/or (5) any other suitable behavior or attribute. The system may be further configured to calculate a consent validity score for each generated consent receipt based at least in part on an analysis of the data subject's behavior (e.g., inputs, lack of inputs, time spent completing the consent form, etc.).

In particular embodiments, the system is configured to monitor the data subject's (e.g., the user's) system inputs while the data subject is competing a particular web form. In particular embodiments actively monitoring the user's system inputs may include, for example, monitoring, recording, tracking, and/or otherwise taking account of the user's system inputs. These system inputs may include, for example: (1) one or more mouse inputs; (2) one or more keyboard (e.g., text) inputs; (3) one or more touch inputs; and/or (4) any other suitable inputs (e.g., such as one or more vocal inputs, etc.). In any embodiment described herein, the system is configured to monitor one or more biometric indicators associated with the user such as, for example, heart rate, pupil dilation, perspiration rate, etc.

In particular embodiments, the system is configured to monitor a user's inputs, for example, by substantially automatically tracking a location of the user's mouse pointer with respect to one or more selectable objects on a display screen of a computing device. In particular embodiments, the one or more selectable objects are one or more selectable objects (e.g., indicia) that make up part of the web form. In still any embodiment described herein, the system is configured to monitor a user's selection of any of the one or more selectable objects, which may include, for example, an initial selection of one or more selectable objects that the user subsequently changes to selection of a different one of the one or more selectable objects.

In any embodiment described herein, the system may be configured to monitor one or more keyboard inputs (e.g., text inputs) by the user that may include, for example, one or more keyboard inputs that the user enters or one or more keyboard inputs that the user enters but deletes without submitting. The user may, for example, initially begin typing a first response, but delete the first response and enter a second response that the user ultimately submits. In various embodiments of the system described herein, the system is configured to monitor the un-submitted first response in addition to the submitted second response.

In still any embodiment described herein, the system is configured to monitor a user's lack of input. For example, a user may mouse over a particular input indicium (e.g., a selection from a drop-down menu, a radio button or other selectable indicia) without selecting the selection or indicia. In particular embodiments, the system is configured to monitor such inputs. As may be understood in light of this disclosure, a user that mouses over a particular selection and lingers over the selection without actually selecting it may, for example, be demonstrating an uncertainty regarding the consent the user is providing.

In any embodiment described herein, the system is configured to monitor any other suitable input by the user. In various embodiments, this may include, for example: (1) monitoring one or more changes to an input by a user; (2) monitoring one or more inputs that the user later removes or deletes; (3) monitoring an amount of time that the user spends providing a particular input; and/or (4) monitoring or otherwise tracking any other suitable information.

In various embodiments, the system is further configured to determine whether a user has accessed and/or actually scrolled through a privacy policy associated with a particular transaction. The system may further determine whether a user has opened an e-mail that includes a summary of the consent provided by the user after submission of the web form. The system may then be configured to use any suitable information related to the completion of the web form or other user activity to calculate a consent validity score. In various embodiments, the consent validity score may indicate, for example: (1) an ease at which the user was able to complete a particular consent form; (2) an indication that a particular consent may or may not have been freely given; (3) etc. In particular embodiments, the system may be configured to trigger a recapture of consent in response to calculating a consent validity score for a particular consent that is below a particular amount. In other embodiment, the system may be configured to confirm a particular user's consent depending on a calculated validity score for the consent.

FIG. 59 depicts an exemplary Consent Validity Scoring Module 5900. As may be understood from FIG. 59, in various embodiments, when executing the Consent Validity Scoring Module 5900, the system begins at Step S910, by identifying and analyzing one or more data subject behaviors while the data subject is providing consent for particular data processing. IN various embodiments, the one or more data subject behaviors may include any suitable data subject behavior described herein. Continuing to Step S920, the system is configured to determine a validity score for the provided consent based at least in part on the analysis at Step S910. The system may then be configured to optionally trigger a recapture of consent based on the determined validity score at Step S930. The system may, for example, be configured to capture a recapture of consent in response to determining that that the validity score is below a predetermined level.

Consent Conversion Optimization Systems

In particular embodiments, any entity (e.g., organization, company, etc.) that collects, stores, processes, etc. personal data may require one or more of: (1) consent from a data subject from whom the personal data is collected and/or processed; and/or (2) a lawful basis for the collection and/or processing of the personal data. In various embodiments, the entity may be required to, for example: (1) demonstrate that a data subject has freely given specific, informed, and unambiguous indication of the data subject's agreement to the processing of his or her personal data (e.g., in the form of a statement or clear affirmative action); (2) demonstrate that the entity received consent from a data subject in a manner clearly distinguishable from other matters (e.g., in an intelligible and easily accessible form, using clear and plain language, etc.); (3) enable a data subject to withdraw consent as easily as the data subject can give consent; (4) separate a data subject's consent from performance under any contract unless such processing is necessary for performance under the contract; etc.

In particular, when storing or retrieving information from an end user's device, an entity may be required to receive consent from the end user for such storage and retrieval. Web cookies are a common technology that may be directly impacted by the consent requirements discussed herein. Accordingly, an entity that use cookies (e.g., on one or more webpages, such as on one or more webpages that make up a website or series of websites) may be required to use one or more banners, pop-ups or other user interfaces on the website (e.g., or a particular webpage of the website) in order to capture consent from end-users to store and retrieve cookie data. In particular, an entity may require consent before storing one or more cookies on a user's device and/or tracking the user via the one or more cookies. In various embodiments, an individual's consent to an entity's use of cookies may require, for example, an explicit affirmative action by the individual (e.g., continued browsing on a webpage and/or series of webpages following display of a cookie notice, clicking an affirmative consent to the use of cookies via a suitable interface, scrolling a webpage beyond a particular point, or undertaking any other suitable activities that requires the individual (e.g., user) to actively proceed with use of the page in order to demonstrate consent (e.g., explicit and/or implied consent) to the use of cookies. In various embodiments, the system may be further configured to optimize a consent interface for, for example, one or more software applications (e.g., one or more mobile applications) or any other suitable application that may require a user to provide consent via any suitable computing device.

The consent required to store and retrieve cookie data may, for example, require a clear affirmative act establishing a freely given, specific, informed and unambiguous indication of a data subject's agreement to the processing of personal data. This may include, for example: (1) ticking a box when visiting an internet website; (2) choosing technical settings for information security services (e.g., via a suitable user interface); (3) performing a scrolling action; (4) clicking on one or more internal links of a webpage; and/or (5) or any other suitable statement or conduct which clearly indicates in this context the data subject's acceptance of the proposed processing of their personal data.

In various embodiments, pre-ticked boxes (or other preselected options) or inactivity may not be sufficient to demonstrate freely given consent. For example, an entity may be unable to rely on implied consent (e.g., “by visiting this website, you accept cookies”). Without a genuine and free choice by data subjects and/or other end users, an entity may be unable to demonstrate valid consent (e.g., and therefore unable to utilize cookies in association with such data subjects and/or end users).

A particular entity may use cookies for any number of suitable reasons. For example, an entity may utilize: (1) one or more functionality cookies (which may, for example, enhance the functionality of one or more webpages or a website by storing user preferences such as the user's location for a weather or news website); (2) one or more performance cookies (which may, for example, help to improve performance of the website on the user's device to provide a better user experience); (3) one or more targeting cookies (which may, for example, be used by advertising partners to build a profile of interests for a user in order to show relevant advertisements through the website; (4) etc. Cookies may also be used for any other suitable reason such as, for example: (1) to measure and improve site quality through analysis of visitor behavior (e.g., through ‘analytics’); (2) to personalize pages and remember visitor preferences; (3) to manage shopping carts in online stores; (4) to track people across websites and deliver targeted advertising; (5) etc.

Under various regulations, an entity may not be required to obtain consent to use every type of cookie utilized by a particular website. For example, strictly necessary cookies, which may include cookies that are necessary for a website to function, may not require consent. An example of strictly necessary cookies may include, for example, session cookies. Session cookies may include cookies that are strictly required for website functionality and don't track user activity once the browser window is closed. Examples of session cookies include: (1) faceted search filter cookies; (2) user authentication cookies; (3) cookies that enable shopping cart functionality; (4) cookies used to enable playback of multimedia content; (5) etc.

Cookies which may trigger a requirement for obtaining consent may include cookies such as persistent cookies. Persistent cookies may include, for example, cookies used to track user behavior even after the use has moved on from a website or closed a browser window.

In order to comply with particular regulations, an entity may be required to: (1) present visitors with information about the cookies a website uses and the purpose of the cookies (e.g., any suitable purpose described herein or other suitable purpose); (2) obtain consent to use those cookies (e.g., obtain separate consent to use each particular type of cookies used by the web site); and (3) provide a mechanism for visitors to withdraw consent (e.g., that is as straightforward as the mechanism through which the visitors initially provided consent). In any embodiment described herein, an entity may only need to receive valid consent from any particular visitor a single time (e.g., returning visitors may not be required to provide consent on subsequent visits to the site). In particular embodiments, although they may not require explicit consent to use, an entity may be required to notify a visitor of any strictly necessary cookies used by a website.

Because entities may desire to maximize a number of end users and other data subjects that provide this valid consent (e.g., for each type of cookie for which consent may be required), it may be beneficial to provide a user interface through which the users are more likely to provide such consent. By receiving consent from a high number of users, the entity may, for example: (1) receive higher revenue from advertising partners; (2) receive more traffic to the website because users of the website may enjoy a better experience while visiting the website; etc. In particular, certain webpage functionality may require the use of cookies in order for a webpage to fully implement the functionality. For example, a national restaurant chain may rely on cookies to identify a user's location in order to direct an order placed via the chain's webpage to the appropriate local restaurant (e.g., the restaurant that is located most proximate to the webpage user). A user that is accessing the restaurant's webpage that has not provided the proper consent to the webpage to utilize the user's location data may become frustrated by the experience because some of the webpage features may appear broken. Such a user may, for example, ultimately exit the webpage, visit a webpage of a competing restaurant, etc. As such, entities may particular desire to increase a number of webpage visitors that ultimately provide the desired consent level so that the visitors to the webpage/website can enjoy all of the intended features of the webpage/website as designed.

In particular embodiments, a consent conversion optimization system is configured to test two or more test consent interfaces against one another to determine which of the two or more consent interfaces results in a higher conversion percentage (e.g., to determine which of the two or more interfaces lead to a higher number of end users and/or data subjects providing a requested level of consent for the creation, storage and use or cookies by a particular website). The system may, for example, analyze end user interaction with each particular test consent interface to determine which of the two or more user interfaces: (1) result in a higher incidence of a desired level of provided consent; (2) are easier to use by the end users and/or data subjects (e.g., take less time to complete, require a fewer number of clicks, etc.); (3) etc.

The system may then be configured to automatically select from between/among the two or more test interfaces and use the selected interface for future visitors of the website.

In particular embodiments, the system is configured to test the two or more test consent interfaces against one another by: (1) presenting a first test interface of the two or more test consent interfaces to a first portion of visitors to a web site/webpage; (2) collecting first consent data from the first portion of visitors based on the first test interface; (3) presenting a second test interface of the two or more test consent interfaces to a second portion of visitors to the website/webpage; (4) collecting second consent data from the second portion of visitors based on the second test interface; (5) analyzing and comparing the first consent data and second consent data to determine which of the first and second test interface results in a higher incidence of desired consent; and (6) selecting between the first and second test interface based on the analysis.

In particular embodiments, the system is configured to enable a user to select a different template for each particular test interface. In any embodiment described herein, the system is configured to automatically select from a plurality of available templates when performing testing. In still any embodiment described herein, the system is configured to select one or more interfaces for testing based on similar analysis performed for one or more other websites.

In still any embodiment described herein, the system is configured to use one or more additional performance metrics when testing particular cookie consent interfaces (e.g., against one another). The one or more additional performance metrics may include, for example: (1) opt-in percentage (e.g., a percentage of users that click the ‘accept all’ button on a cookie consent test banner; (2) average time-to-interaction (e.g., an average time that users wait before interacting with a particular test banner); (3) average time-to-site (e.g., an average time that it takes a user to proceed to normal navigation across an entity site after interacting with the cookie consent test banner; (4) dismiss percentage (e.g., a percentage of users that dismiss the cookie consent banner using the close button, by scrolling, or by clicking on grayed-out website); (5) functional cookies only percentage (e.g., a percentage of users that opt out of any cookies other than strictly necessary cookies); (6) performance opt-out percentage; (7) targeting opt-out percentage; (8) social opt-out percentage; (9) etc. In still other embodiments, the system may be configured to store other consent data related to each of interfaces under testing such as, for example: (1) opt-in percentage by region; (2) opt-in percentage based on known characteristics of the individual data subjects and/or users (e.g., age, gender, profession, etc.); and/or any other suitable data related to consent provision. In such embodiments, the system may be configured to optimize consent conversion by presenting a particular visitor to a webpage that is tailored to the particular visitor based at least in part on both analyzed consent data for one or more test interfaces and on or more known characteristics of the particular visitor (e.g., age range, gender, etc.).

In particular embodiments, the system is configured to utilize one or more performance metrics (e.g., success criteria) for a particular interface based at least in part on one or more regulatory enforcement controls. For example, the system may be configured to optimize consent provision via one or more interfaces that result in a higher level of compliance with one or more particular legal frameworks (e.g., for a particular country). For example, the system may be configured to determine that a first interface has a more optimal consent conversion for a first jurisdiction, even if the first interface results in a lower overall level of consent (e.g., than a second interface) in response to determining that the first interface results in a higher provision of a particular type of consent (e.g., a particular type of consent required to comply with one or more regulations in the first jurisdiction). In particular embodiments, the one or more interfaces (e.g., under testing) may, for example, vary based on: (1) color; (2) text content; (3) text positioning; (4) interface positioning; (5) selector type; (6) time at which the user is presented the consent interface (e.g., after being on a site for at least a particular amount of time such as 5 seconds, 10 seconds, 30 seconds, etc.).

Exemplary Consent Conversion Optimization System Architecture

FIG. 60 is a block diagram of a Consent Conversion Optimization System 6000 according to a particular embodiment. In some embodiments, the Consent Conversion Optimization System 6000 is configured to interface with at least a portion of each respective organization's Privacy Compliance System in order generate, capture, and maintain a record of one or more consents to process, collect, and or store personal data from one or more data subjects.

As may be understood from FIG. 60, the Consent Conversion Optimization System 6000 includes one or more computer networks 6015, a Consent Receipt Management Server 6010, a Consent Interface Management Server 6020 (e.g., which may be configured to enable a user to setup one or more different cookie consent user interfaces using one or more templates), One or More Third Party Servers 6030, one or more databases 6040 (e.g., which may be used to store one or more interfaces for testing), and one or more remote computing devices 6050 (e.g., a desktop computer, laptop computer, tablet computer, etc.). In particular embodiments, the one or more computer networks 6015 facilitate communication between the Consent Receipt Management Server 6010, a Consent Interface Management Server 6020, One or More Third Party Servers 6030, one or more databases 6040, and one or more remote computing devices 6050.

The one or more computer networks 6015 may include any of a variety of types of wired or wireless computer networks such as the Internet, a private intranet, a public switch telephone network (PSTN), or any other type of network. The communication link between Consent Interface Management Server 6020 and Database 6040 may be, for example, implemented via a Local Area Network (LAN) or via the Internet.

Consent Conversion Optimization System

Various embodiments of a Consent Conversion Optimization System 6100 may be implemented in the context of any suitable system (e.g., a privacy compliance system). For example, the Consent Conversion Optimization System 6100 may be implemented to analyze and/or compare one or more test interfaces for obtaining consent from one or more users for the use of cookies in the context of one or more particular websites. In particular embodiments, the system may implement one or more modules in order to at least partially ensure compliance with one or more regulations (e.g., legal requirements) related to the use of cookies (e.g., as discussed herein). Various aspects of the system's functionality may be executed by certain system modules, including a Consent Conversion Optimization Module 6100.

Although this module is presented as a series of steps, it should be understood in light of this disclosure that various embodiments of the Consent Conversion Optimization Module 6100 described herein may perform the steps described below in an order other than in which they are presented. In still other embodiments, the Consent Conversion Optimization Module 6100 may omit certain steps described below. In various other embodiments, the Consent Conversion Optimization Module 300 may perform steps in addition to those described (e.g., such as one or more steps described with respect to one or more other modules, etc.).

FIG. 61 depicts exemplary steps that the system may perform when executing the Consent Conversion Optimization Module 6100. In particular embodiments, a Consent Conversion Optimization Module 6100 is configured to: (1) receive and/or retrieve at least two test interfaces for enabling users to provide cookie consent (e.g., as described herein); (2) perform a/b testing using each of the at least two test interfaces on at least a respective proportion of a population of users that visits a particular website; (3) analyze results of the a/b testing to determine which of the at least two test interfaces leads to a higher incidence of users providing desired consent; and (4) automatically implement the more successful test interface based on the analyzed results. In other embodiments, the system is further configured to: (1) set a threshold and/or minimum sample size of testing for each of the at least two test interfaces (e.g., automatically or based on user input); (2) generate a dashboard configured to display data associated with the analysis; (3) etc.

As may be understood from FIG. 61, when executing the Consent Conversion Optimization Module 6100, the system begins, at Step 6110, by receiving, from a first user via a first computing device (e.g., a remote computing device 6150 such as any of the one or more remote computing devices 6150 shown in FIG. 60), a request to access a website, and, in response to the request, determining whether the first user has previously consented to the use of one or more cookies by the website. In various embodiments, as discussed above, the system may be configured to only present a cookie consent interface to a user that has not: (1) already visited the website and provided consent; (2) already visited the website and elected not to provide consent; (3) already visited the website/webpage and provided less than a level of consent desired by the website administrator; etc.

Continuing to Step 6120, the system is configured to, in response to determining that the first user has not previously consented to the use of one or more cookies by the web site, cause the first computing device to display a first cookie consent interface from a group of at least two test consent interfaces. As may be understood in light of this disclosure, the first cookie consent interface may include a suitable interface (e.g., Interface A stored in the One or More Databases 6140 of FIG. 60) from a group of interfaces under testing. In various embodiments, the system is configured to select the first interface to display to the user randomly from the group of interfaces under testing. In other embodiments, the system is configured to alternate between and/or among test interfaces to display to each new user of (e.g., individual accessing) the website (e.g., via a particular webpage, domain, etc.). In still other embodiments, the system is configured to adhere to a particular proportion of the various interfaces under testing (e.g., ensuring that 50% of website visitors are presented with a first interface and the other 50% are presented with a second interface, etc.). In some embodiments, the system is configured to perform these testing steps until at least a particular number of data points regarding each interface have been collected (e.g., a sufficiently large sample size, a predefined number of tests, etc.). In particular embodiments, the system is configured to present visitors to a particular web domain with a test interface based on a user-provided weight for each particular interface under testing.

In some embodiments, the system may be configured to generate the consent interfaces for testing. In other embodiments, the system is configured to receive one or more test templates created by a user (e.g., using one or more templates, or using any suitable technique described herein).

Next, at Step 6130, the system is configured to collect consent data for the first user based on selections made by the first user via the first cookie consent interface. When collecting consent data, the system may, for example collect data such as: (1) what particular types of cookies the user consented to the use of; (2) location data related to those cookies consented to within the interface (e.g., a location of the interface, a location of a user-selectable button or other indicia for each particular type of cookie, etc.); (3) information associated with how consent is collected (e.g., a check box, slider, radio button, etc.); (4) information associated with a page or screen within the interface on which the various consented to cookie types appear (e.g., as may be understood from FIGS. 62-70); (5) a number of users that provided at least some consent to particular types of cookies through the interface; (6) a number of types of cookies each user consented to, if at all; (7) a geographic location of each user as the system receives (e.g., or doesn't receive) consent from each user; (8) one or more characteristics of each use to which each particular interface is presented (e.g., age, gender, interests, employment information, and any other suitable known information); and (9) any other suitable information.

Continuing to Step 6140, the system is configured to repeat Steps 6110-6130 for a plurality of other users of the website, such that each of the at least two consent interfaces are displayed to at least a portion of the plurality of other users. In various embodiments each of the users of the website include any user that accesses a particular webpage of the website. In particular embodiments, each user of the website includes any user that accesses a particular web domain. As may be understood from this disclosure, the system may, for example, repeat the testing steps described herein until the system has collected at least enough data to determine which of the at least two interfaces results in a higher rate of consent provision by users (e.g., or results in a higher success rate based on a user-provided criteria, such as a criteria provided by a site administrator or other suitable individual).

Returning to Step 6150, the system is configured to analyze the consent data to identify a particular interface of the at least two consent interfaces under testing that results in a more desired level of consent (e.g., that meets the success criteria). The system may, for example, determine which interface resulted in a greater percentage of obtained consent. The system may also determine which interface resulted in a higher provision of a particular type of consent. For example, the system may determine which interface led to provision, by end users, of a higher rate of consent for particular types of cookies (e.g., performance cookies, targeting cookies, etc.). The system may be further configured to analyze, based on other consent data, whether provision of consent may be related to particular aspects of the user interface (e.g., a location of a radio button or other input for providing the consent, etc.). The system may further be configured to cross reference the analyzed consent data against previously recorded consent data (e.g., for other interfaces).

In response to identifying the particular interface at Step 6150, the system is configured, at Step 6160, to store the particular interface in memory for use as a site-wide consent interface for all users of the website. The system may, for example, utilize the more ‘successful’ interface for all future visitors of the website (e.g., because the use of such an interface may lead to an overall higher rate of consent than another interface or combination of different interfaces).

Finally, at Step 6170, the system may be configured to optionally repeat Steps 6110-6160 using one or more additional test consent interfaces. The system may, for example, implement a particular interface for capturing consent after performing the initial analysis described above, and then introduce a potential new test interface that is developed later on. The system may then test this new test interface against the original choice to determine whether to switch to the new interface or continue using the existing one.

Exemplary End-User Experience of Consent Interfaces Under Testing

FIGS. 62-70 depict exemplary screen displays and interfaces that a user may encounter when accessing a web site (e.g., a particular webpage of a web site) that requires the user to provide consent for the use of cookies. As may be understood from these figures, particular interfaces may utilize different arrangements and input types in order to attempt to obtain consent from end-users. FIG. 62, for example, depicts an exemplary cookie banner 6200, which may, for example, appear on any suitable portion of webpage (e.g., on the top of the webpage, on the bottom of the webpage, in the center or center portion of the webpage, as a pop up, integrated within the webpage itself, etc.). The banner 6200 may, for example, appear on a user's initial visit to a particular webpage. As may be understood from FIG. 62, a cookie banner 6200 such as the one depicted may enable a user (e.g., a visitor to a webpage) to accept all cookies with the click of a single button 6205. The banner 6200 may include a link 6210 to the entity that maintains the webpage's Cookie Policy.

In FIGS. 63 and 64, for example, the interface displays information about all types of cookies on a single screen along with an ability for the user to provide consent for each specific cookie type through the single interface screen. FIGS. 63 and 64 differ, however, in the manner in which the user provides consent. In FIG. 63, the interface 6300 uses sliders, while in FIG. 64, the interface 6400 utilizes radio buttons. As may be understood from FIG. 63, a user is unable to opt out of strictly necessary cookies, but may select an appropriate slider 6305, 6310 to enable/disable functional cookies and/or performance cookies. As may be understood from FIG. 62, a user is also unable to opt out of strictly necessary cookies, but may select an appropriate radio button 6405, 6410 to enable/disable functional cookies and/or performance cookies. In a particular implementation, the system may be configured to test the interfaces of FIGS. 63 and 64 against one another to determine whether users are more likely to provide the desired consent using one type of selector or another.

FIGS. 65-68 depict an exemplary interface with which a user can provide consent for the use of cookies according to another example. In the example shown in these Figures, specific types of cookies are separated in the interface between different pages that the user must individually select, providing consent for each cookie type on the respective screen (e.g., page). As may be understood from these Figures, the interfaces contain information about the types of cookies and the purpose of their use, while enabling the user to provide consent for each type of cookie. The user may, for example, need to cycle within a privacy preference center among the following interfaces shown in FIGS. 65-68, and 70: (1) an initial privacy interface 6500 that describes an overall privacy policy (e.g., in FIG. 65); (2) a strictly necessary cookie interface 6600 that provides information about strictly necessary cookies used by the webpage, but does not enable the user to opt out of strictly necessary cookies (e.g., because strictly necessary cookies may not require consent from users (e.g., in FIG. 66); (3) a performance cookie interface 6700 that provides information about performance cookies used by the webpage, and enables the user to activate a slider 6705 to enable/disable performance cookies (e.g., in FIG. 6700); (4) a targeting cookie interface 6800 that provides information about targeting cookies used by the webpage, and enables the user to activate a slider 6805 to enable/disable targeting cookies (e.g., in FIG. 68); (5) an advertising cookie interface 7000 that provides information about advertising cookies used by the webpage, and enables the user to activate a slider 7005 to enable/disable all advertising cookies or activate individual sliders 7010 to enable/disable particular advertising cookies (e.g., in FIG. 70); (6) etc. FIG. 69 depicts an interface 6900 such as the targeting cookie interface 6800 of FIG. 68, with the slider 6905 set to disable targeting cookies.

The system, in various embodiments, may be configured to test an interface in which all cookie information is shown on a single page (e.g., such as the interfaces shown in FIG. 63 or 64) against the type of interface shown in FIGS. 65-68 to determine whether one or the other is more likely to result in a higher rate of consent by end-users. In particular embodiments, the system may further analyze whether particular types of cookies (e.g., presented on earlier pages/screens of the interface or occurring earlier on the listing of cookies on the left-hand side of the interface) are more likely to be consented to by users.

FIG. 70 depicts a user interface 7000 where a user can provide consent for a particular type of cookies, and then separately consent to each particular cookie of that type used by the web site.

These various types of interfaces and others may be utilized by the system in testing one or more ways in which to optimize consent receipt from end users in the context of the system described herein.

Data Processing Systems for Verifying an Age of a Data Subject

In particular embodiments, a data processing consent management system may be configured to utilize one or more age verification techniques to at least partially authenticate the data subject's ability to provide valid consent (e.g., under one or more prevailing legal requirements). For example, according to one or more particular legal or industry requirements, an individual (e.g., data subject) may need to be at least a particular age (e.g., an age of majority, an adult, over 18, over 21, or any other suitable age) in order to provide valid consent.

In various embodiments, a consent receipt management system may be implemented in the context of any suitable privacy management system that is configured to ensure compliance with one or more legal or industry standards related to the collection and/or storage of private information (e.g., such as personal data). In particular embodiments, the system is configured to manage one or more consent receipts between a data subject and an entity. In various embodiments, a consent receipt may include a record (e.g., a data record stored in memory and associated with the data subject) of consent, for example, as a transactional agreement where the data subject is already identified or identifiable as part of the data processing that results from the provided consent.

As may be understood from this disclosure, any particular transaction may record and/or require one or more valid consents from the data subject. For example, the system may require a particular data subject to provide consent for each particular type of personal data that will be collected as part of the transaction. The system may, in various embodiments, be configured to prompt the data subject to provide valid consent, as described herein.

The system may, for example, be configured to track data on behalf of an entity that collects and/or processes personal data related to: (1) who consented to the processing or collection of personal data (e.g., the data subject themselves or a person legally entitled to consent on their behalf such as a parent, guardian, etc.); (2) when the consent was given (e.g., a date and time); (3) what information was provided to the consenter at the time of consent (e.g., a privacy policy, what personal data would be collected following the provision of the consent, for what purpose that personal data would be collected, etc.); (4) how consent was received (e.g., one or more copies of a data capture form, webform, etc. via which consent was provided by the consenter); (5) when consent was withdrawn (e.g., a date and time of consent withdrawal if the consenter withdraws consent); and/or (6) any other suitable data related to receipt or withdrawal of consent.

In some embodiments, the system may be configured to verify the age of the data subject. The system may, for example, be configured to validate a consent provided by a data subject by authenticating an age of the data subject. For example, the system may be configured to confirm, using any suitable technique described herein, that the data subject has reached the age of majority in the jurisdiction in which the data subject resides (e.g., is not a minor).

A type of transaction that the data subject is consenting to may require the data subject to be of at least a certain age for the data subject's consent to be considered valid by the system. Similarly, the system may determine whether the data subject's consent is valid based on the data subject's age in response to determining one or more age restrictions on consent in a location (e.g., jurisdiction) in which the data subject resides, is providing the consent, etc. In still other embodiments, one or more age restrictions may apply to a particular transaction (e.g., entry into a sweepstakes, consent that involves access to mature content, etc.).

For example, a data subject that is under the age of eighteen in a particular country may not be legally able to provide consent for credit card data to be collected as part of a transaction. The system may be configured to determine an age for valid consent for each particular type of personal data that will be collected as part of any particular transaction based on one or more factors. These factors may include, for example, the transaction and type of personal data collected as part of the transaction, the country where the transaction is to occur and the country of the data subject, and the age of the data subject, among others.

In various implementations, the system may be configured to verify the age of a data subject by providing a prompt for the data subject to provide a response to one or more questions. The response to each of the one or more questions may prompt the data subject to provide a selection (e.g., from a list) or input of data (e.g., input within a text box). In some implementations, the system may generate a logic problem or quiz as the prompt. The logic problem or quiz may be tailored to identify an age of the data subject or whether the data subject is younger or older than a threshold age (e.g., the age for valid consent for the particular type of personal data that will be collected as part of the transaction). The logic problem or quiz may be randomized or specific to a data subject, and in some embodiments, the logic problem or quiz may include mathematics or reading comprehension problems.

In some embodiments, the system may verify the age of a data subject in response to prompting the data subject to provide identifying information of the data subject (e.g., via a response to one or more questions), and then accessing a public third-party database to determine an age of the data subject. The identifying information may include, for example, a name, address, phone number, etc. of the data subject. In some implementations, the system may erase the provided identifying information from storage within the system after the age of the data subject is verified.

The system may, for example, be configured to: (1) receive, from a data subject, a request to enter into a particular transaction with an entity, the transaction involving the collection of personal data associated with the data subject by the entity; (2) in response to receiving the request, determining whether the collection of personal data by the entity under the transaction requires the data subject to be at least a particular age; (3) at least partially in response to determining that the transaction requires the data subject to be at least the particular age, using one or more age verification techniques to confirm the age of the data subject; (4) in response to determining, using the one or more age verification techniques, that the data subject is at least the particular age, storing a consent receipt that includes data associate with the entity, the data subject, the age verification, and the transaction; and (5) initiating the transaction between the data subject and the entity.

In particular embodiments, a particular entity may systematically confirm an age (e.g., or prompt for parental consent as described below) as a matter of course. For example, particular entities may provide one or more products or services that are often utilized and/or consumed by minors (e.g., toy companies). Such entities may, for example, utilize a system described herein such that the system is configured to automatically verify the age of every data subject that attempts to enter into a transaction with the entity. For example, Lego may require any user registering for the Lego website to verify that they are over 18, or, alternatively, to use one of the guardian/parental consent techniques described below to ensure that the entity has the consent of a guardian of the data subject in order to process the data subject's data.

In various embodiments, the one or more age verification techniques may include, for example: (1) comparing one or more pieces of information provided by the data subject to one or more pieces of publicly available information (e.g., in one or more databases, credit bureau directories, etc.); (2) prompting the data subject to provide one or more response to one or more age-challenge questions (e.g., brain puzzles, logic problems, math problems, vocabulary questions, etc.); (3) prompting the data subject to provide a copy of one or more government issued identification cards, receiving an input or image of the one or more government identification cards, confirming the authenticity of the one or more government identification cards, and confirming the age of the data subject based on information from the one or more government identification cards; (4) etc. In response to determining that the data subject is not at least the particular required age, the system may be configured to prompt a guardian or parent of the data subject to provide consent on the data subject's behalf (e.g., as described below).

The system may, for example, be configured to track data on behalf of an entity that collects and/or processes personal data related to: (1) who consented to the processing or collection of personal data (e.g., the data subject themselves or a person legally entitled to consent on their behalf such as a parent, guardian, etc.); (2) when the consent was given (e.g., a date and time); (3) what information was provided to the consenter at the time of consent (e.g., a privacy policy, what personal data would be collected following the provision of the consent, for what purpose that personal data would be collected, etc.); (4) how consent was received (e.g., one or more copies of a data capture form, webform, etc. via which consent was provided by the consenter); (5) when consent was withdrawn (e.g., a date and time of consent withdrawal if the consenter withdraws consent); (6) an age of the consenting data subject; and/or (7) any other suitable data related to receipt or withdrawal of consent.

In some embodiments, the system may be configured to verify the age of the data subject. The system may, for example, be configured to validate a consent provided by a data subject by authenticating an age of the data subject. For example, the system may be configured to confirm, using any suitable technique described herein, that the data subject has reached the age of majority in the jurisdiction in which the data subject resides (e.g., is not a minor). In other embodiments, the system may be configured to confirm that the data subject has reached any other suitable age which may be required under the data processing transaction.

A type of transaction that the data subject is consenting to may require the data subject to be of at least a certain age for the data subject's consent to be considered valid by the system. Similarly, the system may determine whether the data subject's consent is valid based on the data subject's age in response to determining one or more age restrictions on consent in a location (e.g., jurisdiction) in which the data subject resides, is providing the consent, etc. In still other embodiments, one or more age restrictions may apply to a particular transaction (e.g., entry into a sweepstakes, consent that involves access to mature content, etc.).

For example, a data subject that is under the age of eighteen in a particular country may not be legally able to provide consent for credit card data to be collected as part of a transaction. The system may be configured to determine an age for valid consent for each particular type of personal data that will be collected as part of any particular transaction based on one or more factors. These factors may include, for example, the transaction and type of personal data collected as part of the transaction, the country where the transaction is to occur and the country of the data subject, and the age of the data subject, among others.

The system may, for example, be configured to: (1) receive, from a data subject, a request to enter into a particular transaction with an entity, the transaction involving the collection of personal data associated with the data subject by the entity; (2) in response to receiving the request, determining whether the collection of personal data by the entity under the transaction requires the data subject to be at least a particular age; (3) at least partially in response to determining that the transaction requires the data subject to be at least the particular age, using one or more age verification techniques to confirm the age of the data subject; (4) in response to determining, using the one or more age verification techniques, that the data subject is at least the particular age, storing a consent receipt that includes data associate with the entity, the data subject, the age verification, and the transaction; and (5) initiating the transaction between the data subject and the entity.

In particular embodiments, a particular entity may systematically confirm an age (e.g., or prompt for parental consent as described below) as a matter of course. For example, particular entities may provide one or more products or services that are often utilized and/or consumed by minors (e.g., toy companies). Such entities may, for example, utilize a system described herein such that the system is configured to automatically verify the age of every data subject that attempts to enter into a transaction with the entity. For example, Lego may require any user registering for the Lego web site to verify that they are over 18, or, alternatively, to confirm that a parent/guardian over the age of 18 has authorized a minor (e.g., under 18, under 13, etc.) to register. This may, for example, ensure that the entity has the consent of a guardian of the data subject in order to process the data subject's data.

In various embodiments, the one or more age verification techniques may include, for example, analyzing one or more images of the data subject in order to estimate an age of the data subject based at least in part on one or more features of the data subject (e.g., one or more facial features, a determined height, or any other suitable feature). In particular embodiments, the system is configured to transmit one or more images provided and/or taken by the data subject to a third-party system for analysis. In particular embodiments, the system may, in response to transmitting the one or more images to the third-party service, receive image analysis data. The image analysis data may include, for example: (1) a determined age of the data subject; (2) a determined range within which the data subject's age falls; (3) a certainty level in the determined age and/or range; and/or (4) any other suitable data related to the analysis of the one or more images.

In particular embodiments, the system is configured to request that the data subject provide one or more additional images in response to determining that the certainty level is below a predefined level (e.g., because the system is unable to determine with at least a particular level of certainty that a determined age is accurate).

In response to determining that the data subject is not at least the particular required age, the system may be configured to prompt a guardian or parent of the data subject to provide consent on the data subject's behalf. Alternatively, in response to determining that the data subject is not at least the particular required age, the system may be configured to reject the data subject's request to imitate the transaction.

In various embodiments, the system may be configured to perform one or more additional pieces of analysis to determine, for example: (1) whether the one or more images provided by the data subject are of the data subject (e.g., as opposed to one or more other individuals); (2) whether the one or more images are sufficiently recent to determine an age (e.g., or age range) of the data subject with at least a particular confidence level; (3) whether the data subject has provided one or more images that meet one or more requirements provided by the system; (4) etc.

In various embodiments, the system may, for example, be configured to analyze one or more pieces of data associated with a provided image (e.g., EXIF data) to determine: (1) a date and/or time at which the image was taken; (2) a location at which the image was taken; and/or (3) any other suitable data. The system may then compare data related to a time and/or date of the image capture to a current time and/or date to determine whether the image is sufficiently recent to serve as a valid image for confirming the age of the data subject. The system may further compare location data for the photo to determine whether the location is sufficiently close to a determined location of a computing device from which the user is requesting to initiate the transaction. In this way, the system may be configured to confirm that the data subject has taken and uploaded (e.g., provided) an image that was taken sufficiently recently and that was taken (e.g., and/or more likely taken) by the individual requesting to initiate the transaction.

For example, a user requesting to initiate a transaction via a computing device without an integrated imaging device may: (1) take an image of themselves on a second computing device (e.g., with an integrated or other imaging device); (2) transfer the image from the second computing device to the first computing device on which the user is attempting to initiate the transaction that requires age verification of consent; and (3) provide the image for analysis by the system from the first computing device. By determining location data of the image, the system may confirm that the user has just taken the photo (e.g., or that the photo was taken sufficiently recently) and that the photo was taken in proximity to the first computing device (e.g., even if the photo was taken with a second computing device). In this way, the system may preclude a data subject from providing a photo of another person (e.g., that was taken at a different time, in a different location, using a different computing device by a friend or relative, or simply downloaded from the internet). In this way, the system may be configured to reduce an incidence of users just providing images of other people, taking images of an image, etc.).

In particular embodiments, the system may prompt the user to provide (e.g., take and/or upload) an image in which the user is performing a particular action (e.g., holding up a particular number of fingers, making a particular face, turning their head in a particular direction, framing the image in a particular way, etc.). In this way, the system may further reduce a chance that a user will find an image of another individual to provide to the system that is performing the requested action. The system may then use one or more image analysis techniques (e.g., and/or provide the image to a third-party AI imaging service for analysis) to confirm that the user is performing the requested action in the image in additional to determining the user's age in the image). The third-party AI imaging service may, for example, include a service such as Microsoft Face, AWS Facial Recognition, etc.

In still other embodiments, the system may be configured to prompt the data subject to provide one or more series of images in which the data subject is progressively performing a progressive series of action (e.g., turning one way and then another, or any other actions). The system may, for example, prompt the user to provide one or more additional images in response to determining that a confidence level of a determined age is below a pre-determined level.

In any embodiment described herein, the system may, for example, be configured to: (1) determine whether the computing device via which the data subject is attempting to initiate the transaction requiring the data subject to be at least a particular age (e.g., provide consent requiring the data subject be at least a particular age) includes an integrated imaging device (e.g., one or more cameras such as on a laptop computer, smartphone, tablet computer, etc.); (2) in response to determining that the computing device via which the data subject is attempting to initiate the transaction comprises an integrated imaging device, prompt the data subject to take a new photo using the integrated imaging device in response to the data subject requesting to initiate the transaction. In such embodiments, requiring the data subject to provide at least one image via an integrated imaging device may, for example, prevent the data subject from uploading one or more saved images of a different individual in order to get around an age requirement (e.g., one or more images of an older person).

In particular embodiments, the system may be configured to prompt the data subject to take at least two or more images using the integrated imaging device for analysis. In such embodiments, requiring the data subject to provide at least two images via the integrated imaging device may, for example, at least partially prevent the data subject from, for example, using the integrated imaging device to take an image of an image (e.g., such as a printed image, image on a display screen, etc.) that includes an individual other than the data subject who may, for example, be older than the required age (e.g., while the data subject may be younger). In response to receiving the at least two images, the system may be configured to analyze the at least two images to determine whether: (1) each of the at least two images include the same person; (2) the at least two images are not the same images; and (3) the person in each of the at least two images are at least the required age.

FIG. 71 depicts an exemplary screen display that a data subject may encounter when providing consent to the processing of personal data. As shown in FIG. 71, a data subject may provide particular personal data (e.g., first and last name, email, country of residence, date of birth, etc.) when signing up for a free trial with a particular entity via a trial signup interface 7100. A data subject may encounter a similar interface when initiating any other transaction with an entity such as, for example: (1) accessing the entity's website; (2) signing up for a user account with the entity; (3) signing up for a mailing list with the entity; (4) a free trial sign up; (5) product registration; and/or (6) any other suitable transaction that may result in collection and/or processing of personal data, by the entity, about the data subject (e.g., personal data for which the entity may require consent from the data subject in order to legally process the data or process the data in compliance with one or more regulations).

As may be understood in light of this disclosure, the free trial may constitute a transaction between the data subject (e.g., user) and a particular entity providing the free trial. In various embodiments, the data subject (e.g., user) may encounter the interface shown in FIG. 71 in response to accessing a website associated with the particular entity for the free trial (e.g., a signup page). In still other embodiments a user may encounter a cookie consent notice or other transaction consent notice (e.g., as shown in FIG. 62), which may require the user to consent to the use of one or more cookies by a particular website. In various embodiments, a website may include an age restriction (e.g., such as in the case of an alcohol company, pornographic website, or other website with mature content), which may, for example, depend on a jurisdiction from which a user accesses the site (e.g., 13 and up, 18 and up, 21 and up, etc.).

In particular embodiments, the interface 7100 is configured to enable the user (e.g., data subject) to provide the information required to sign up for the free trial. As shown in FIG. 71, the interface further includes a listing of particular things that the data subject is consenting to (e.g., the processing of first name, last name, e-mail address, location, age, country of residence, etc.) as well as one or more purposes for the processing of such data (e.g., marketing information, directed advertising, weekly newsletter, etc.). The interface may further include a link to a Privacy Policy that governs the use of the information, one or more terms and conditions that govern the transaction, etc.

In various embodiments, in response to the user (e.g., data subject) submitting the webform shown in FIG. 71, the system is configured to confirm the age provided by the data subject. A type of transaction that the data subject is consenting to may require the data subject to be of at least a certain age for the data subject's consent to be considered valid by the system. Similarly, the system may determine whether the data subject's consent is valid based on the data subject's age in response to determining one or more age restrictions on consent in a location (e.g., jurisdiction) in which the data subject resides, is providing the consent, etc.

For example, a data subject that is under the age of eighteen in a particular country may not be legally able to provide consent for credit card data to be collected as part of a transaction. The system may be configured to determine an age for valid consent for each particular type of personal data that will be collected as part of any particular transaction based on one or more factors. These factors may include, for example, the transaction and type of personal data collected as part of the transaction, the country where the transaction is to occur and the country of the data subject, and the age of the data subject, among others.

In various implementations, the system may be configured to verify the age of a data subject by utilizing one or more third party AI imaging services (e.g., via an application programming interface, by transmitting one or more images to the imaging service for analysis, performing the analysis on one or more local devices, etc.). As may be understood in light of this disclosure, a particular type of transaction that the data subject may be consenting to may require the data subject to be of at least a certain age for the data subject's consent to be considered valid by the system. Similarly, the system may determine whether the data subject's consent is valid based on the data subject's age in response to determining one or more age restrictions on consent in a location (e.g., jurisdiction) in which the data subject resides, is providing the consent, etc.

For example, a data subject that is under the age of eighteen in a particular country may not be legally able to provide consent for credit card data to be collected as part of a transaction. The system may be configured to determine an age for valid consent for each particular type of personal data that will be collected as part of any particular transaction based on one or more factors. These factors may include, for example, the transaction and type of personal data collected as part of the transaction, the country where the transaction is to occur and the country of the data subject, and the age of the data subject, among others.

In various implementations, the system may be configured to verify the age of a data subject by prompting the data subject to provide one or more images of the data subject's face for use in an image analysis to determine the data subject's estimated age. FIG. 72 depicts an interface via which the data subject may provide one or more images of the data subject's face. The system may then be configured to perform analysis on the image in order to estimate the data subject's age based on one or more features of the data subject's face. In particular embodiments, the system is configured to interface with one or more third party image analysis services. In such embodiments, the system is configured to transmit the one or more images provided and/or taken by the data subject to the third-party image analysis system (e.g., and/or access the service using an API or other technique. The system may then, in response, receive an estimated age from the third-party system.

In the interface shown in FIG. 72, the system may provide a user interface 400 for the data subject to upload and/or take a photo of the data subject's face for analysis by the system. As may be understood from this figure, the interface 7200 may enable the data subject to upload a photo (e.g., by accessing computer memory via an upload photo button 7210 in order to select a previously taken photo). In various embodiments the computer memory may include memory operably coupled to a computing device (e.g., mobile computing device) via which the data is accessing the user interface. In still other embodiments, the system may enable the data subject to access one or more cloud storage services from which the use may select a stored photo for analysis. The interface 7200 may further provide a take photo button 7215. In response to the user selecting the take photo button 7215, the system may be configured to access one or more cameras (e.g., one or more cameras integrated into the computing device via which the data is accessing the user interface) in order to enable the data subject to take one or more photos of their face for analysis. The one or more cameras may include, for example, a front-facing camera on a mobile computing device. In the embodiment shown in FIG. 72, the interface 7200 may include a preview window 7205 via which the data subject can preview a selected, stored photo prior to submission for analysis (e.g., and/or view a photo that their computing device would take while using an integrated camera).

After taking and/or selecting a photo for analysis, the data subject may submit the photo (e.g., by selecting a sign-up button). In other embodiments, the system may be configured to analyze the provided photo (e.g., or transmit the photo to a third-party system for analysis) substantially in real time in response to the user selecting and/or taking the photo. The system may then be configured to prevent the user from selecting the sign-up button in response to the image analysis determining that the data subject is not at least an age required for initiating the transaction (e.g., providing valid consent) and/or determining that the data subject's provided age is other than a determined age range by the image analysis.

In still other embodiments, a user, when attempting to access a website or consent to one or more particular cookies, may need to provide one or more photos. The system may then use the one or more photos to verify the user's age. For example, the system may prompt the user to provide a series of photos (e.g., two or more) from an integrated imaging device of the computing device from which the user is attempting to access the website, provide cookie consent, etc.

In various embodiments, the system is configured to prompt the user to take an updated image in response to determining that a confidence score in a determined age is below a particular threshold. In such embodiments, the system may be configured to continue to request updated images until the system is able to determine an age of the data subject with at least a particular level of confidence.

In various embodiments, the system is configured to prompt the user to perform one or more particular actions while capturing one or more images (e.g., turn their head a particular direction, hold up a particular number of fingers, etc.). In various embodiments, the system is configured to analyze the one or more images to determine whether the data subject is performing the requested action (e.g., gesture). The system may, for example, request a particular gesture or action in order to ensure that the user is note trying to provide a fake or doctored photograph in order to get around one or more age requirements.

In a particular embodiment, a client-side application may be configured to capture one or more images and extract key data from the one or more images for analysis. In various embodiments, the system may then transmit the key data to a backend server (e.g., third party service) for age analysis. The system may then be configured top receive an age determination from the backend server.

Data Processing Systems for Prompting a Guardian to Provide Consent on Behalf of a Minor Data Subject

In various embodiments, the system may require guardian consent (e.g., parental consent) for a data subject. The system may prompt the data subject to initiate a request for guardian consent or the system may initiate a request for guardian consent without initiation from the data subject (e.g., in the background of a transaction). In some embodiments, the system may require guardian consent when a data subject is under the age for valid consent for the particular type of personal data that will be collected as part of the particular transaction. The system may use the any age verification method described herein to determine the age of the data subject. Additionally, in some implementations, the system may prompt the data subject to identify whether the data subject is younger, at least, or older than a particular age (e.g., an age for valid consent for the particular type of personal data that will be collected as part of the particular transaction), and the system may require guardian consent when the data subject identifies an age younger than the particular age.

In various embodiments, the system may be configured to communicate via electronic communication with the identified guardian (e.g., parent) of the data subject. The electronic communication may include, for example, email, phone call, text message, message via social media or a third-party system, etc. In some embodiments, the system may prompt the data subject to provide contact information for the data subject's guardian. The system may provide the electronic communication to the contact information provided by the data subject, and prompt the guardian to confirm they are the guardian of the data subject. In some embodiments, the system may provide a unique code (e.g., a six-digit code, or other unique code) as part of the electronic communication provided to the guardian. The guardian may then provide the received unique code to the data subject, and the system may enable the data subject to input the unique code to the system to confirm guardian consent. In some embodiments, the system may use blockchain between an electronic device of the guardian and the system and/or an electronic device of the data subject to confirm guardian consent.

In various implementations, the system may include an electronic registry of guardians for data subjects that may not be of age for valid consent for particular types of personal data to be collected as part of the particular transaction. For example, guardians may access the electronic registry to identify one or more data subjects for which they are a guardian. Additionally, the guardian may identify one or more types of personal data and transactions for which the guardian will provide guardian consent. Further, in some implementations, the system may use previous authorizations of guardian consent between a guardian and particular data subject to identify the guardian of the particular data subject, and the guardian-data subject link may be created in the electronic registry of the system.

The system may further be configured to confirm an age of the individual (e.g., parent or guardian) providing consent on the data subject's behalf. The system may confirm the individuals age using any suitable age verification technique described herein.

In response to receiving valid consent from the data subject, the system is configured to transmit the unique transaction ID and the unique consent receipt key back to the third-party consent receipt management system for processing and/or storage. In other embodiments, the system is configured to transmit the transaction ID to a data store associated with one or more entity systems (e.g., for a particular entity on behalf of whom the third-party consent receipt management system is obtaining and managing validly received consent). The system may be further configured to transmit a consent receipt to the data subject which may include, for example: (1) the unique transaction ID; (2) the unique consent receipt key; and/or (3) any other suitable data related to the validly provided consent.

CONCLUSION

Although embodiments above are described in reference to various privacy compliance monitoring systems, it should be understood that various aspects of the system described above may be applicable to other privacy-related systems, or to other types of systems, in general.

While this specification contains many specific embodiment details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems may generally be integrated together in a single software product or packaged into multiple software products.

Many modifications and any embodiment described herein of the invention will come to mind to one skilled in the art to which this invention pertains having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the invention is not to be limited to the specific embodiments disclosed and that modifications and any embodiment described herein are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for the purposes of limitation. 

What is claimed is:
 1. A method comprising: responsive to a request to initiate a transaction between an entity and a data subject, generating a consent receipt set comprising a consent receipt identifier, a transaction identifier based on the transaction, and a subject identifier based on the data subject; requesting, by the computing hardware, at least one piece of identifying information regarding the data subject; receiving, by the computing hardware, the at least one piece of identifying information comprising an image of the data subject; determining, by the computing hardware, based at least in part on the at least one piece of identifying information, an age of the data subject by: causing an artificial intelligence image system to generate a prediction usable for determining the age of the data subject by providing the image of the data subject to the artificial intelligence image system for analysis; and receiving, from the artificial intelligence image system, the prediction; determining, by the computing hardware, based on the age of the data subject and the transaction, whether the data subject meets one or more age criteria for processing personal data under the transaction; and in response to determining the data subject does not meet the one or more age criteria for the processing of personal data under the transaction, modifying, by the computing hardware, the consent receipt set to prevent a computing system from providing the data subject with access to functionality requiring valid consent.
 2. The method of claim 1, wherein: the consent receipt set comprises a consent status and generating the consent receipt set comprises initially setting, by the computing hardware, the consent status to valid in response to the request to initiate the transaction; and modifying the consent receipt set to prevent the computing system from providing the data subject with access to the functionality requiring valid consent comprises modifying, by the computing hardware, the consent status to invalid in response to determining the data subject does not meet the one or more age criteria.
 3. The method of claim 1, wherein: the method further comprises determining, by the computing hardware, image capture data associated with the image of the data subject, the image capture data comprising at least one of a capture time of the image of the data subject or an image capture location of the image of the data subject; and determining whether the data subject meets the one or more age criteria for processing the personal data under the transaction is further based on the image capture data.
 4. The method of claim 3, wherein: the image capture data comprises the capture time of the image of the data subject; and determining whether the data subject meets the one or more age criteria for processing the personal data under the transaction comprises determining whether the capture time of the image of the data subject is within a threshold amount of time of a time of the request to initiate the transaction.
 5. The method of claim 1, wherein: the prediction comprises a confidence level; and the method further comprises: determining, by the computing hardware based on the confidence level, whether the prediction satisfies a threshold confidence level; responsive to determining that the prediction does not satisfy the threshold confidence level, requesting at least one second piece of identifying information; and determining, by the computing hardware, based at least in part on the at least one second piece of identifying information, the age of the data subject by: causing the artificial intelligence image system to generate a second prediction usable for determining the age of the data subject by providing the second image of the data subject to the artificial intelligence image system for analysis; and receiving, from the artificial intelligence image system, the second prediction.
 6. The method of claim 5, wherein the at least one second piece of identifying information comprises a second image of the data subject.
 7. The method of claim 1, further comprising: causing, by the computing hardware, the artificial intelligence image system to generate a second prediction usable for determining whether the data subject is performing a particular action in the image of the data subject; and determining, by the computing hardware, whether the data subject meets the one or more age criteria for processing personal data under the transaction based on the second prediction.
 8. A system comprising: a non-transitory computer-readable medium storing instructions; and a processing device communicatively coupled to the non-transitory computer-readable medium, wherein, the processing device is configured to execute the instructions and thereby perform operations comprising: responsive to a request to initiate a transaction between an entity and a data subject, generating a consent receipt set comprising a consent receipt identifier, a transaction identifier based on the transaction, and a subject identifier based on the data subject; receiving a piece of identifying information for the data subject; causing a third-party artificial intelligence system to generate an output related to determining an age of the data subject by providing the piece of identifying information to the third-party artificial intelligence system for analysis; receiving the output related to determining the age of the data subject from the third-party artificial intelligence system; determining, based on the output, whether the data subject satisfies an age criterion for the transaction; and in response to determining that the data subject does not meet the age criterion, modifying the consent receipt set to prevent a computing system from providing the data subject with access to functionality requiring valid consent.
 9. The system of claim 8, wherein: the output comprises a request for additional identifying information; the operations further comprise: receiving the additional identifying information for the data subject; causing the third-party artificial intelligence system to generate a second output related to determining the age of the data subject by providing the additional identifying information to the third-party artificial intelligence system for analysis; and receiving the second output; and determining whether the data subject satisfies the age criterion for the transaction is further based on the second output.
 10. The system of claim 8, wherein the output comprises at least one of a predicted age of the data subject, a predicted age range for the data subject, or an age prediction confidence level for the data subject.
 11. The system of claim 10, wherein: the consent receipt set comprises a consent status and generating the consent receipt set comprises initially setting the consent status to valid in response to the request to initiate the transaction; and modifying the consent receipt set to prevent the computing system from providing the data subject with access to the functionality requiring valid consent comprises modifying the consent status to invalid in response to determining the data subject does not meet the age criterion.
 12. The system of claim 8, wherein the piece of identifying information comprises an image of the data subject.
 13. The system of claim 12, wherein: the operations further comprise determining image capture data associated with the image of the data subject, the image capture data comprising at least one of a capture time of the image of the data subject or an image capture location of the image of the data subject; and determining whether the data subject satisfies the age criterion is further based on the image capture data.
 14. The system of claim 13, wherein: the image capture data comprises the image capture location of the image; and determining whether the data subject satisfies the age criterion comprises determining whether the image capture location is within a particular distance of a determined location of a computing device from which the request to initiate the transaction originated.
 15. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by processing hardware, configure the processing hardware to perform operations comprising: responsive to a request to initiate a transaction between an entity and a data subject, generating a consent receipt set, wherein: the request comprises a transaction parameter, a data subject parameter, and a consent parameter indicating consent by the data subject to processing of personal data received via a computer network as part of the transaction; and the consent receipt set comprises a consent receipt identifier, a transaction identifier based on the transaction parameter, a consent status based on the consent parameter, and a subject identifier based on the data subject parameter; receiving an image of the data subject; causing an artificial image system to generate an output related to determining an age of the data subject by providing the image of the data subject to the artificial intelligence image system for analysis; receiving the output related to determining the age of the data subject from the artificial intelligence image system; determining, based on the output, whether the data subject satisfies an age criterion for the transaction; and in response to determining that the data subject does not meet the age criterion, modifying the consent status to prevent a computing system from providing the data subject with access to functionality requiring valid consent.
 16. The non-transitory computer-readable medium of claim 15, wherein the output comprises at least one of a predicted age of the data subject, a predicted age range for the data subject, or an age prediction confidence level for the data subject.
 17. The non-transitory computer-readable medium of claim 15, wherein the output comprises a prediction usable for determining the age of the data subject.
 18. The non-transitory computer-readable medium of claim 15, wherein the output comprises a second prediction usable for determining whether the data subject is performing a particular action in the image of the data subject.
 19. The non-transitory computer-readable medium of claim 15, wherein: the operations further comprise determining image capture data associated with the image of the data subject, the image capture data comprising at least one of a capture time of the image of the data subject or an image capture location of the image of the data subject; and determining whether the data subject satisfies the age criterion is further based on the image capture data
 20. The non-transitory computer-readable medium of claim 15, wherein: the output comprises a request for additional identifying information; the operations further comprise: receiving the additional identifying information for the data subject; causing the third-party artificial intelligence system to generate a second output related to determining the age of the data subject by providing the additional identifying information to the third-party artificial intelligence system for analysis; and receiving the second output; and determining whether the data subject satisfies the age criterion for the transaction is further based on the second output. 